BCI2000 Wiki - User contributions [en]

User Reference:Filters

2009-02-17T15:01:06Z

Sbriskin: /* Configuration Overview */

''Filters'' are the most important building blocks of a BCI2000 system.
This document describes the ''filter'' concept for users who will configure BCI2000 for their own experiments, and provides links to the documentation pages of individual filters, describing each filter's purpose and parameterization.

==Data Processing as a Pipe==
Each of the three BCI2000 core modules contains a ''chain of filters'', i.e. a sequence of filters forming a pipe where, basically, brain signal data enter on one side, and a processed version of these data leaves on the other side.

The notion of a ''pipe'' implies that, for each portion of data entering on the input side, there will be exactly one portion of output data on the output side.
This is analogous to a water pipe: unlike a water stream (a brook, or a river), it is impossible to insert or remove water from inside the pipe without breaking it.
Similarly, albeit signal portions may change their shape on their way through the pipe (filter chain), it is impossible to insert or remove any of them.
Thus, each data portion acquired by the data acquisition module will run through the entire BCI2000 system, being processed by a sequence of filters.

==Configuration Overview==
In principle, these filters may be placed in any order by the writer (programmer) of a BCI2000 module, and it is [[Programming Reference:Filter Chain|technically easy]] to change the number, and order, of filters.
However, not all combinations or orderings of filters make sense.

The flowchart shows modules and filters for BCI2000's configurations included in the core distribution:
<div align="center">[[Image:FilterPipelineFlowchart.png|792pxx260px|Flowchart of the Data moving through the Filter Pipeline]] 
'''Flowchart of the Data moving through the Filter Pipeline'''</div>

==Data Acquisition Module==
In the data acquisition (source) module, the DataIOFilter manages data acquisition and storage in a general manner.
Actual acquisition of data is provided by ADC filters,
actual writing into data files is done by FileWriter filters representing various [[User Reference:Data File Formats|Data Formats]].
In addition to managing the operation of ADC and FileWriter filters, the DataIOFilter handles signal calibration into physical units (typically, <math>\mu V</math>), and visualization of the source signal.

Filter documentation for source modules is available for
===General Filters===
*[[User Reference:DataIOFilter]]: Storage and acquisition management, calibration.
*[[User Reference:SourceFilter]]: HP/LP/notch filtering prior to storage.
*[[User Reference:AlignmentFilter]]: Temporal alignment.
*[[User Reference:TransmissionFilter]]: Subset selection for online processing.

===Data Acquisition===
*[[User Reference:SignalGeneratorADC]]: A signal generator for testing purposes.
*[[User Reference:gUSBampADC]]: Interface to the g.tec gUSBamp amplifier.
*[[User Reference:gMOBIlabADC]]: Interface to the g.tec gMOBIlab amplifier.
*[[Contributions:ADCs]]: Contributed code interfacing to other hardware.

===Data Storage===
*[[User Reference:BCI2000FileWriter]]: BCI2000 data format.
*[[User Reference:EDFFileWriter]]: European Data Format.
*[[User Reference:GDFFileWriter]]: General Data Format for Biosignals.
*[[User Reference:NullFileWriter]]: Suppressing file output.
*[[Contributions:FileWriters]]: Contributed code supporting other output formats.

==Signal Processing Module==
In the signal processing module, brain signals are filtered spatially and temporally, resulting in a set of extracted features. In the Classifier, these features are used to differentiate amongst a small number of mental states (classes). Finally, the Normalizer adjusts the Classifier's output to zero mean and unit variance.

Filter documentation for signal processing filters is available for
===Feature Extraction===
*[[User Reference:SpatialFilter]]: Instantaneous (i.e., sample-wise) linear transformation of brain signal input.
*[[User Reference:ARFilter]]: Auto-regression based spectral amplitude estimator.
*[[User Reference:FFTFilter]]: Short-term FFT for display or feature extraction purposes.
*[[User Reference:P3TemporalFilter]]: Segmenting and averaging for ERP feature extraction.

===Translation Algorithm===
*[[User Reference:LinearClassifier]]: Linear projection of signal features onto low-dimensional classification space (control signal).
*[[User Reference:Normalizer]]: Adjustment of control signal to zero mean/unit variance.

===Miscellaneous Filters===
*[[User Reference:LPFilter]]: Temporal low-pass filtering at any stage of processing.
*[[User Reference:ConditionalIntegrator]]: An offline replacement for cursor movement in an online task.
*[[User Reference:StateTransform]]: An offline replacement for hitting targets in an online task.
*[[User Reference:ExpressionFilter]]: Computes filter output using arithmetic expressions.
*[[User Reference:MatlabFilter]]: Using MATLAB to implement online processing in the pipeline.

==Application Module==
Basically, the application module contains a single filter that handles trial sequencing and brain signal feedback.
In the standard configuration, this ''task'' filter is surrounded by ConnectorInput and ConnectorOutput filters. These connector filters allow for exchanging data with external software over a UDP based socket protocol.

Filter documentation of application module filters is available for
===Stimulus Presentation and Feedback===
*[[User Reference:CursorTask]]: Feedback of brain signals in form of up to 3-dimensional cursor movement.
*[[User Reference:StimulusPresentationTask]]: Sequential presentation of stimuli.
*[[User Reference:P3SpellerTask]]: A P300 speller matrix application.
===External Interfaces===
*[[User Reference:ConnectorOutput]]: Reports state information to a UDP socket.
*[[User Reference:ConnectorInput]]: Sets state information according to input from a UDP socket.
*[[User Reference:KeystrokeFilter]]: Translates state information into simulated keyboard input.
*[[User Reference:JoystickFilter]]: Records joystick movements into a state.
*[[User Reference:KeyLogFilter]]: Records keyboard and mouse key presses into a state.
*[[User Reference:MouseFilter]]: Records mouse pointer coordinates into a state.

===Internationalization===
*[[User Reference:Localization]]: Translating subject-visible messages.

==See also==
[[Technical Reference:Core Modules]]

[[Category:Contents]][[Category:Filters]]

User Reference:Filters

2009-02-17T14:57:29Z

Sbriskin: /* Configuration Overview */

''Filters'' are the most important building blocks of a BCI2000 system.
This document describes the ''filter'' concept for users who will configure BCI2000 for their own experiments, and provides links to the documentation pages of individual filters, describing each filter's purpose and parameterization.

==Data Processing as a Pipe==
Each of the three BCI2000 core modules contains a ''chain of filters'', i.e. a sequence of filters forming a pipe where, basically, brain signal data enter on one side, and a processed version of these data leaves on the other side.

The notion of a ''pipe'' implies that, for each portion of data entering on the input side, there will be exactly one portion of output data on the output side.
This is analogous to a water pipe: unlike a water stream (a brook, or a river), it is impossible to insert or remove water from inside the pipe without breaking it.
Similarly, albeit signal portions may change their shape on their way through the pipe (filter chain), it is impossible to insert or remove any of them.
Thus, each data portion acquired by the data acquisition module will run through the entire BCI2000 system, being processed by a sequence of filters.

==Configuration Overview==
In principle, these filters may be placed in any order by the writer (programmer) of a BCI2000 module, and it is [[Programming Reference:Filter Chain|technically easy]] to change the number, and order, of filters.
However, not all combinations or orderings of filters make sense.

The table shows modules and filters for BCI2000 configurations included in the core distribution:
<div align="center">[[Image:FilterPipelineFlowchart.png|792pxx260px|Flowchart of the Data moving through the Filter Pipeline]] 
'''Flowchart of the Data moving through the Filter Pipeline'''</div>

==Data Acquisition Module==
In the data acquisition (source) module, the DataIOFilter manages data acquisition and storage in a general manner.
Actual acquisition of data is provided by ADC filters,
actual writing into data files is done by FileWriter filters representing various [[User Reference:Data File Formats|Data Formats]].
In addition to managing the operation of ADC and FileWriter filters, the DataIOFilter handles signal calibration into physical units (typically, <math>\mu V</math>), and visualization of the source signal.

Filter documentation for source modules is available for
===General Filters===
*[[User Reference:DataIOFilter]]: Storage and acquisition management, calibration.
*[[User Reference:SourceFilter]]: HP/LP/notch filtering prior to storage.
*[[User Reference:AlignmentFilter]]: Temporal alignment.
*[[User Reference:TransmissionFilter]]: Subset selection for online processing.

===Data Acquisition===
*[[User Reference:SignalGeneratorADC]]: A signal generator for testing purposes.
*[[User Reference:gUSBampADC]]: Interface to the g.tec gUSBamp amplifier.
*[[User Reference:gMOBIlabADC]]: Interface to the g.tec gMOBIlab amplifier.
*[[Contributions:ADCs]]: Contributed code interfacing to other hardware.

===Data Storage===
*[[User Reference:BCI2000FileWriter]]: BCI2000 data format.
*[[User Reference:EDFFileWriter]]: European Data Format.
*[[User Reference:GDFFileWriter]]: General Data Format for Biosignals.
*[[User Reference:NullFileWriter]]: Suppressing file output.
*[[Contributions:FileWriters]]: Contributed code supporting other output formats.

==Signal Processing Module==
In the signal processing module, brain signals are filtered spatially and temporally, resulting in a set of extracted features. In the Classifier, these features are used to differentiate amongst a small number of mental states (classes). Finally, the Normalizer adjusts the Classifier's output to zero mean and unit variance.

Filter documentation for signal processing filters is available for
===Feature Extraction===
*[[User Reference:SpatialFilter]]: Instantaneous (i.e., sample-wise) linear transformation of brain signal input.
*[[User Reference:ARFilter]]: Auto-regression based spectral amplitude estimator.
*[[User Reference:FFTFilter]]: Short-term FFT for display or feature extraction purposes.
*[[User Reference:P3TemporalFilter]]: Segmenting and averaging for ERP feature extraction.

===Translation Algorithm===
*[[User Reference:LinearClassifier]]: Linear projection of signal features onto low-dimensional classification space (control signal).
*[[User Reference:Normalizer]]: Adjustment of control signal to zero mean/unit variance.

===Miscellaneous Filters===
*[[User Reference:LPFilter]]: Temporal low-pass filtering at any stage of processing.
*[[User Reference:ConditionalIntegrator]]: An offline replacement for cursor movement in an online task.
*[[User Reference:StateTransform]]: An offline replacement for hitting targets in an online task.
*[[User Reference:ExpressionFilter]]: Computes filter output using arithmetic expressions.
*[[User Reference:MatlabFilter]]: Using MATLAB to implement online processing in the pipeline.

==Application Module==
Basically, the application module contains a single filter that handles trial sequencing and brain signal feedback.
In the standard configuration, this ''task'' filter is surrounded by ConnectorInput and ConnectorOutput filters. These connector filters allow for exchanging data with external software over a UDP based socket protocol.

Filter documentation of application module filters is available for
===Stimulus Presentation and Feedback===
*[[User Reference:CursorTask]]: Feedback of brain signals in form of up to 3-dimensional cursor movement.
*[[User Reference:StimulusPresentationTask]]: Sequential presentation of stimuli.
*[[User Reference:P3SpellerTask]]: A P300 speller matrix application.
===External Interfaces===
*[[User Reference:ConnectorOutput]]: Reports state information to a UDP socket.
*[[User Reference:ConnectorInput]]: Sets state information according to input from a UDP socket.
*[[User Reference:KeystrokeFilter]]: Translates state information into simulated keyboard input.
*[[User Reference:JoystickFilter]]: Records joystick movements into a state.
*[[User Reference:KeyLogFilter]]: Records keyboard and mouse key presses into a state.
*[[User Reference:MouseFilter]]: Records mouse pointer coordinates into a state.

===Internationalization===
*[[User Reference:Localization]]: Translating subject-visible messages.

==See also==
[[Technical Reference:Core Modules]]

[[Category:Contents]][[Category:Filters]]

File:FilterPipelineFlowchart.png

2009-02-17T14:07:35Z

Sbriskin:

User Reference:Filters

2009-02-13T20:26:07Z

Sbriskin: /* Configuration Overview */

''Filters'' are the most important building blocks of a BCI2000 system.
This document describes the ''filter'' concept for users who will configure BCI2000 for their own experiments, and provides links to the documentation pages of individual filters, describing each filter's purpose and parameterization.

==Data Processing as a Pipe==
Each of the three BCI2000 core modules contains a ''chain of filters'', i.e. a sequence of filters forming a pipe where, basically, brain signal data enter on one side, and a processed version of these data leaves on the other side.

The notion of a ''pipe'' implies that, for each portion of data entering on the input side, there will be exactly one portion of output data on the output side.
This is analogous to a water pipe: unlike a water stream (a brook, or a river), it is impossible to insert or remove water from inside the pipe without breaking it.
Similarly, albeit signal portions may change their shape on their way through the pipe (filter chain), it is impossible to insert or remove any of them.
Thus, each data portion acquired by the data acquisition module will run through the entire BCI2000 system, being processed by a sequence of filters.

==Configuration Overview==
In principle, these filters may be placed in any order by the writer (programmer) of a BCI2000 module, and it is [[Programming Reference:Filter Chain|technically easy]] to change the number, and order, of filters.
However, not all combinations or orderings of filters make sense.

The table shows modules and filters for BCI2000 configurations included in the core distribution:

{| border="1" style="text-align:center"
!   || colspan="3" | Data Acquisition
! colspan="6" | Signal Processing
! colspan="3" | Application
|-
! SMR Feedback

| rowspan="3" |
{| border="0" style="text-align:center"
| colspan="2" | [[User Reference:DataIOFilter|Data IO Filter]]
|-
| ADC || FileWriter
|}
| rowspan="3" | [[User Reference:AlignmentFilter|Alignment Filter]]
| rowspan="3" | [[User Reference:TransmissionFilter|Transmission Filter]]
| rowspan="3" | [[User Reference:SpatialFilter|Spatial Filter]]
| [[User Reference:ARFilter|AR Filter]] or [[User Reference:FFTFilter|FFT Filter]]
| rowspan="3" | [[User Reference:LinearClassifier|Linear Classifier]]
| rowspan="3" | [[User Reference:LPFilter|LP Filter]]
| rowspan="3" | [[User Reference:ExpressionFilter|Expression Filter]]
| rowspan="3" | [[User Reference:Normalizer|Normalizer]]
| rowspan="3" | [[User Reference:ConnectorInput|Connector Input]]
| [[User Reference:CursorTask|Cursor Task]]
| rowspan="3" | [[User Reference:ConnectorOutput|Connector Output]]
|-
! P3 Speller
| rowspan="2" | [[User Reference:P3TemporalFilter|P3 Temporal Filter]]
| [[User Reference:P3SpellerTask|P3 Speller Task]]
|-
! Stimulus Presentation
| [[User Reference:StimulusPresentationTask|Stimulus Presentation Task]]
|}

==Data Acquisition Module==
In the data acquisition (source) module, the DataIOFilter manages data acquisition and storage in a general manner.
Actual acquisition of data is provided by ADC filters,
actual writing into data files is done by FileWriter filters representing various [[User Reference:Data File Formats|Data Formats]].
In addition to managing the operation of ADC and FileWriter filters, the DataIOFilter handles signal calibration into physical units (typically, <math>\mu V</math>), and visualization of the source signal.

Filter documentation for source modules is available for
===General Filters===
*[[User Reference:DataIOFilter]]: Storage and acquisition management, calibration.
*[[User Reference:SourceFilter]]: HP/LP/notch filtering prior to storage.
*[[User Reference:AlignmentFilter]]: Temporal alignment.
*[[User Reference:TransmissionFilter]]: Subset selection for online processing.

===Data Acquisition===
*[[User Reference:SignalGeneratorADC]]: A signal generator for testing purposes.
*[[User Reference:gUSBampADC]]: Interface to the g.tec gUSBamp amplifier.
*[[User Reference:gMOBIlabADC]]: Interface to the g.tec gMOBIlab amplifier.
*[[Contributions:ADCs]]: Contributed code interfacing to other hardware.

===Data Storage===
*[[User Reference:BCI2000FileWriter]]: BCI2000 data format.
*[[User Reference:EDFFileWriter]]: European Data Format.
*[[User Reference:GDFFileWriter]]: General Data Format for Biosignals.
*[[User Reference:NullFileWriter]]: Suppressing file output.
*[[Contributions:FileWriters]]: Contributed code supporting other output formats.

==Signal Processing Module==
In the signal processing module, brain signals are filtered spatially and temporally, resulting in a set of extracted features. In the Classifier, these features are used to differentiate amongst a small number of mental states (classes). Finally, the Normalizer adjusts the Classifier's output to zero mean and unit variance.

Filter documentation for signal processing filters is available for
===Feature Extraction===
*[[User Reference:SpatialFilter]]: Instantaneous (i.e., sample-wise) linear transformation of brain signal input.
*[[User Reference:ARFilter]]: Auto-regression based spectral amplitude estimator.
*[[User Reference:FFTFilter]]: Short-term FFT for display or feature extraction purposes.
*[[User Reference:P3TemporalFilter]]: Segmenting and averaging for ERP feature extraction.

===Translation Algorithm===
*[[User Reference:LinearClassifier]]: Linear projection of signal features onto low-dimensional classification space (control signal).
*[[User Reference:Normalizer]]: Adjustment of control signal to zero mean/unit variance.

===Miscellaneous Filters===
*[[User Reference:LPFilter]]: Temporal low-pass filtering at any stage of processing.
*[[User Reference:ConditionalIntegrator]]: An offline replacement for cursor movement in an online task.
*[[User Reference:StateTransform]]: An offline replacement for hitting targets in an online task.
*[[User Reference:ExpressionFilter]]: Computes filter output using arithmetic expressions.
*[[User Reference:MatlabFilter]]: Using MATLAB to implement online processing in the pipeline.

==Application Module==
Basically, the application module contains a single filter that handles trial sequencing and brain signal feedback.
In the standard configuration, this ''task'' filter is surrounded by ConnectorInput and ConnectorOutput filters. These connector filters allow for exchanging data with external software over a UDP based socket protocol.

Filter documentation of application module filters is available for
===Stimulus Presentation and Feedback===
*[[User Reference:CursorTask]]: Feedback of brain signals in form of up to 3-dimensional cursor movement.
*[[User Reference:StimulusPresentationTask]]: Sequential presentation of stimuli.
*[[User Reference:P3SpellerTask]]: A P300 speller matrix application.
===External Interfaces===
*[[User Reference:ConnectorOutput]]: Reports state information to a UDP socket.
*[[User Reference:ConnectorInput]]: Sets state information according to input from a UDP socket.
*[[User Reference:KeystrokeFilter]]: Translates state information into simulated keyboard input.
*[[User Reference:JoystickFilter]]: Records joystick movements into a state.
*[[User Reference:KeyLogFilter]]: Records keyboard and mouse key presses into a state.
*[[User Reference:MouseFilter]]: Records mouse pointer coordinates into a state.

===Internationalization===
*[[User Reference:Localization]]: Translating subject-visible messages.

==See also==
[[Technical Reference:Core Modules]]

[[Category:Contents]][[Category:Filters]]

User Tutorial:Obtaining P300 Parameters in a Calibration Session

2009-02-05T19:07:44Z

Sbriskin: /* Analyzing The Calibration Session with Offline Analysis */

==Obtaining P300 Parameters in the Calibration Session==

Although the basic properties of the P300 evoked potential are the same for all individuals, the response's latency, width, and spatial pattern varies, and adaptation to individual parameters improves accuracy.

Thus, it is necessary to obtain these individual parameters prior to performing spelling experiments.

==Design of Calibration Session==

During the calibration session, the volunteer is asked to spell out a given word by using a P300 character matrix, pictured below. During each run, the volunteer is asked to focus on the next letter in the word they are spelling, as the rows and columns flash randomly and successively so that sometimes the flashing corresponds to the column or row containing the target character and sometimes it will not. As the volunteer counts the number of times the desired letter in the word flashes, a P300 response is generated. The purpose of the calibration session is to identify those features that discriminate between the desired and undesired rows/columns.

[[Image:P3SpellerMatrix.PNG|298px]]

After the first few runs are collected, an analysis tool will be used to generate a configuration file with weights that will determine what portions of the input data correspond to selecting the desired letter. These weights are applied to the configuration session for one more run, and then the data from this run is also analyzed with the MatLab tool, and the number of flashes needed to ensure 100% accuracy is determined. The second configuration file generated and applied. This final set of parameters are saved as that volunteer’s parameter file for future spelling sessions.

==Performing the Configuration Session==

*Start BCI2000 by running <tt>batch/P3Speller_<Your_Amplifier>.bat</tt>
*Press '''Config''', and load the baseline parameters for copy spelling that you made earlier.
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
*In the '''Application''' tab:
**Make sure that ''InterpretMode'' is set to <tt>copy mode</tt>, and ''DisplayResults'' (directly below InterpretMode) is unchecked
**Find the ''TextToSpell'' field. This should be set to ‘THE’, and you will be changing it after each run.
*Press '''Set Config''' to apply this configuration.

*Request that the volunteer sit in a relaxed position, and that the volunteer not move or speak during the runs.
*Turning off or dimming the lights can improve volunteer focus and performance.
*Showing the brain wave readouts to the volunteer can drive in the message of how artifact-generating behavior can be detrimental to the data.
*Press '''Start''' to show the flashing character matrix, and describe what the volunteer is expected to do.
*After you’ve explained the procedure, click '''Suspend''' to stop the process.
*Delete that run of data (The file should be named <tt>data\P300\<Volunteer's Initials>001R01.dat</tt>)

*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>QUICK</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>BROWN</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>FOX</tt>.
*Press '''Start''' to record the run.
*Once this recording has finished, close BCI2000 and locate the saved data files

==Analyzing The Calibration Session with Offline Analysis==
We will now perform an ‘Offline Analysis’ with a tool provided with the BCI2000 system.
[[Image:OfflineGUI.PNG|right]]
*Run <tt>tools/OfflineAnalysis/OfflineAnalysis.bat</tt>
*In the ''Analysis Domain'' field, choose '''Time (P300)'''
*In the ''Acquisition Type'' field, choose '''EEG'''
*Next to ''Spatial Filter'', choose '''Common Average Reference (CAR)'''
*For '''Trial Change Condition''' enter <tt>states.StimulusBegin == 1</tt>
*For '''Target Condition 1''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 1)</tt>
*For '''Target Condition Label 1''' enter <tt>Attended Stimuli</tt>
**‘Attended Stimuli’ refers to the letter or character the person is counting the flashes of, and triggers when the correct stimulus is shown
*For '''Target Condition 2''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 0)</tt>
*For '''Target Condition Label 2''' enter <tt>Unattended Stimuli</tt>
**‘Unattended stimulus’ refers to the letters or characters the person is not counting the flashes of, and triggers when an incorrect stimulus is shown
*Click the '''Add''' button by ''Data Files''
*In this new dialog, select all of the data files taken during this configuration session, and click '''Open'''

*Click '''Generate Plots''' and wait for the feature plot to appear

{|
|height=160px|
|}

[[Image:FeaturesPlot.PNG|right|500px]]

*When this is complete, you will see a feature plot similar to the one to the right. The vertical axis corresponds to the locations while the horizontal corresponds to the time delay after the stimulus. The color coding shows the r-squared value of that datapoint, the darker the color indicating a higher predictability that it is produced by the desired letter being flashed.
*The darkest of these r-squared values between 250 and 550ms are what we are interested in. Pick the 4 points with the largest r-squared values between these times and record their time points and channels. The plot’s ''Data Cursor'' tool (Tools Menu → Data Cursor) allows for discrete identification of time points.

*With these four points, close the feature plot, and enter the channel numbers of the data points you found into the ''Waveform Channels'' field and the time points into the ''Topo Times'' field.
**In the example above, the four best data points have r-squared values <tt>0.02218</tt>, <tt>0.02179</tt>, <tt>0.01928</tt>, and <tt>0.019</tt>, occur at times <tt>388.7ms</tt>, <tt>392.6ms</tt>, <tt>384.8ms</tt>, and <tt>365.2ms</tt> respectively, and all four are detected by channel six. <tt>6, 6, 6, 6</tt> would be entered into ''Waveform Channels'' and <tt>388.7, 392.6, 384.8, 365.2</tt> would be entered into the ''Topo Times'' field. The order of the data points here does not matter, only that they are in the same order in both fields.
**If there are less than four points that seem appropriate, either they are at the wrong electrodes, at the wrong times, or simply have too low of an r-squared value, that is fine, three or two values can work, though the fewer values used the lower the accuracy will be.
*Click '''Generate Plots''' to create the features plot again with a set of four graphs that show the correlation between the selected times after the desired stimuli is given (the red line) and the brain’s responses to when the desired stimuli is not given (the blue line).
**As seen below, the attended-stimuli reaction will typically be stronger than the unattended-stimuli, but in some cases the reverse is true. If the ‘unattended’ curve is larger than the ‘attended’ curve then make a note of this before moving on. The waveform seen here is similar to the others generated, only one is shown here for simplicity.
**Additionally before moving on, determine the location of the response seen. The P300 response is generally observed centered on the Cz electrode, or just behind and directly in between the ears, and does not involve the frontal regions of the brain. Assuming these characteristics are present, it is proper to proceed.

[[Image:Waveform.PNG|center|800px]]

*Now we will save these customizations to a volunteer-specific parameters file that will allow the volunteer to free-spell with very high accuracy.
*Start BCI2000 using <tt>batch/P3Speller_<Your_Amplifier>.bat</tt> file
*Click '''Config''', and load the <tt>P300_copy_speller_<Your_Amplifier>.prm</tt> made previously
*Under the '''Filtering''' tab, click the '''Edit Matrix''' button by ''Classifier'' near the bottom
*Change this matrix to have 4 columns and however many rows as values as you are using, and click '''Set New Matrix Size'''
**In the first column, labeled ''Input Channel'', enter the channel of the first value you use
**In the second column, labeled ''Input Element (bin)'', enter the time of the best classification, immediately followed with <tt>ms</tt>, as in <tt>388.7ms</tt>
**In the third column, enter 1 as the output channel
**In the fourth column, enter 1 if the ''Attended'' line was larger than the ''Unattended'' line, -1 if the ''unattended'' line was larger than the ''attended'' line
**Repeat these steps for the remaining rows
*Close this matrix, and click '''Save Parameters''' to save this file, naming it however you deem fit
*Use this new parameter file to repeat the configuration session a few times, adding new rows to the classifier matrix each time for the new data points to be utilized.
*When accuracy is reliably above 90%, click '''Config,''' and click on the '''Application''' tab:
**''NumberOfSequences'' to this number as well
**Delete the contents of the ''Text to Spell'' field
**Set ''InterpretMode'' → <tt>online free mode</tt>
**Make sure the ''DisplayResults'' box is checked
**Click on '''Edit Matrix''' next to ''TargetDefinitions'' and scroll to the bottom:
***In the first column replace <tt>9</tt> with <tt>BS</tt>
***In the second column replace <tt>9</tt> with <tt><BS></tt>
*Click '''Save Parameters''', and change the <tt>copy_spell</tt> portion of this parameter file name to <tt>free_spell</tt>
*This parameter file is now ready to use for that specific volunteer for future P300 spelling experiments

One program contributed to, and provided with, the BCI2000 system is the P300 GUI. This program utilizes MatLab R2007a to automatically sift through data files for datapoints indicative of a positive P300 reaction, and build a classifier matrix with those datapoints. The largest benefit to using this program is the obviated need for repeat configuration sessions, at the expense of decreased hands-on experience with the BCI2000 program itself. For a tutorial on how to perform the offline analysis with this GUI program, [[User Tutorial:P300 GUI for Offline Analysis Tutorial|please click here]].

To continue onto performing P300 spelling experiments, continue to [[User Tutorial:Performing a P300 Spelling Session|Performing a P300 Spelling Session]].

User Tutorial:Obtaining P300 Parameters in a Calibration Session

2009-02-05T19:03:24Z

Sbriskin: /* Analyzing The Calibration Session with Offline Analysis */

==Obtaining P300 Parameters in the Calibration Session==

Although the basic properties of the P300 evoked potential are the same for all individuals, the response's latency, width, and spatial pattern varies, and adaptation to individual parameters improves accuracy.

Thus, it is necessary to obtain these individual parameters prior to performing spelling experiments.

==Design of Calibration Session==

During the calibration session, the volunteer is asked to spell out a given word by using a P300 character matrix, pictured below. During each run, the volunteer is asked to focus on the next letter in the word they are spelling, as the rows and columns flash randomly and successively so that sometimes the flashing corresponds to the column or row containing the target character and sometimes it will not. As the volunteer counts the number of times the desired letter in the word flashes, a P300 response is generated. The purpose of the calibration session is to identify those features that discriminate between the desired and undesired rows/columns.

[[Image:P3SpellerMatrix.PNG|298px]]

After the first few runs are collected, an analysis tool will be used to generate a configuration file with weights that will determine what portions of the input data correspond to selecting the desired letter. These weights are applied to the configuration session for one more run, and then the data from this run is also analyzed with the MatLab tool, and the number of flashes needed to ensure 100% accuracy is determined. The second configuration file generated and applied. This final set of parameters are saved as that volunteer’s parameter file for future spelling sessions.

==Performing the Configuration Session==

*Start BCI2000 by running <tt>batch/P3Speller_<Your_Amplifier>.bat</tt>
*Press '''Config''', and load the baseline parameters for copy spelling that you made earlier.
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
*In the '''Application''' tab:
**Make sure that ''InterpretMode'' is set to <tt>copy mode</tt>, and ''DisplayResults'' (directly below InterpretMode) is unchecked
**Find the ''TextToSpell'' field. This should be set to ‘THE’, and you will be changing it after each run.
*Press '''Set Config''' to apply this configuration.

*Request that the volunteer sit in a relaxed position, and that the volunteer not move or speak during the runs.
*Turning off or dimming the lights can improve volunteer focus and performance.
*Showing the brain wave readouts to the volunteer can drive in the message of how artifact-generating behavior can be detrimental to the data.
*Press '''Start''' to show the flashing character matrix, and describe what the volunteer is expected to do.
*After you’ve explained the procedure, click '''Suspend''' to stop the process.
*Delete that run of data (The file should be named <tt>data\P300\<Volunteer's Initials>001R01.dat</tt>)

*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>QUICK</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>BROWN</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>FOX</tt>.
*Press '''Start''' to record the run.
*Once this recording has finished, close BCI2000 and locate the saved data files

==Analyzing The Calibration Session with Offline Analysis==
We will now perform an ‘Offline Analysis’ with a tool provided with the BCI2000 system.
[[Image:OfflineGUI.PNG|right]]
*Run <tt>tools/OfflineAnalysis/OfflineAnalysis.bat</tt>
*In the ''Analysis Domain'' field, choost '''Time (P300)'''
*In the ''Acquisition Type'' field, choose '''EEG'''
*Next to ''Spatial Filter'', choose '''Common Average Reference (CAR)'''
*For '''Trial Change Condition''' enter <tt>states.StimulusBegin == 1</tt>
*For '''Target Condition 1''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 1)</tt>
*For '''Target Condition Label 1''' enter <tt>Attended Stimuli</tt>
**‘Attended Stimuli’ refers to the letter or character the person is counting the flashes of, and triggers when the correct stimulus is shown
*For '''Target Condition 2''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 0)</tt>
*For '''Target Condition Label 2''' enter <tt>Unattended Stimuli</tt>
**‘Unattended stimulus’ refers to the letters or characters the person is not counting the flashes of, and triggers when an incorrect stimulus is shown
*Click the '''Add''' button by ''Data Files''
*In this new dialog, select all of the data files taken during this configuration session, and click '''Open'''

*Click '''Generate Plots''' and wait for the feature plot to appear

{|
|height=160px|
|}

[[Image:FeaturesPlot.PNG|right|500px]]

*When this is complete, you will see a feature plot similar to the one to the right. The vertical axis corresponds to the locations while the horizontal corresponds to the time delay after the stimulus. The color coding shows the r-squared value of that datapoint, the darker the color indicating a higher predictability that it is produced by the desired letter being flashed.
*The darkest of these r-squared values between 250 and 550ms are what we are interested in. Pick the 4 points with the largest r-squared values between these times and record their time points and channels. The plot’s ''Data Cursor'' tool (Tools Menu → Data Cursor) allows for discrete identification of time points.

*With these four points, close the feature plot, and enter the channel numbers of the data points you found into the ''Waveform Channels'' field and the time points into the ''Topo Times'' field.
**In the example above, the four best data points have r-squared values <tt>0.02218</tt>, <tt>0.02179</tt>, <tt>0.01928</tt>, and <tt>0.019</tt>, occur at times <tt>388.7ms</tt>, <tt>392.6ms</tt>, <tt>384.8ms</tt>, and <tt>365.2ms</tt> respectively, and all four are detected by channel six. <tt>6, 6, 6, 6</tt> would be entered into ''Waveform Channels'' and <tt>388.7, 392.6, 384.8, 365.2</tt> would be entered into the ''Topo Times'' field. The order of the data points here does not matter, only that they are in the same order in both fields.
**If there are less than four points that seem appropriate, either they are at the wrong electrodes, at the wrong times, or simply have too low of an r-squared value, that is fine, three or two values can work, though the fewer values used the lower the accuracy will be.
*Click '''Generate Plots''' to create the features plot again with a set of four graphs that show the correlation between the selected times after the desired stimuli is given (the red line) and the brain’s responses to when the desired stimuli is not given (the blue line).
**As seen below, the attended-stimuli reaction will typically be stronger than the unattended-stimuli, but in some cases the reverse is true. If the ‘unattended’ curve is larger than the ‘attended’ curve then make a note of this before moving on. The waveform seen here is similar to the others generated, only one is shown here for simplicity.
**Additionally before moving on, determine the location of the response seen. The P300 response is generally observed centered on the Cz electrode, or just behind and directly in between the ears, and does not involve the frontal regions of the brain. Assuming these characteristics are present, it is proper to proceed.

[[Image:Waveform.PNG|center|800px]]

*Now we will save these customizations to a volunteer-specific parameters file that will allow the volunteer to free-spell with very high accuracy.
*Start BCI2000 using <tt>batch/P3Speller_<Your_Amplifier>.bat</tt> file
*Click '''Config''', and load the <tt>P300_copy_speller_<Your_Amplifier>.prm</tt> made previously
*Under the '''Filtering''' tab, click the '''Edit Matrix''' button by ''Classifier'' near the bottom
*Change this matrix to have 4 columns and however many rows as values as you are using, and click '''Set New Matrix Size'''
**In the first column, labeled ''Input Channel'', enter the channel of the first value you use
**In the second column, labeled ''Input Element (bin)'', enter the time of the best classification, immediately followed with <tt>ms</tt>, as in <tt>388.7ms</tt>
**In the third column, enter 1 as the output channel
**In the fourth column, enter 1 if the ''Attended'' line was larger than the ''Unattended'' line, -1 if the ''unattended'' line was larger than the ''attended'' line
**Repeat these steps for the remaining rows
*Close this matrix, and click '''Save Parameters''' to save this file, naming it however you deem fit
*Use this new parameter file to repeat the configuration session a few times, adding new rows to the classifier matrix each time for the new data points to be utilized.
*When accuracy is reliably above 90%, click '''Config,''' and click on the '''Application''' tab:
**''NumberOfSequences'' to this number as well
**Delete the contents of the ''Text to Spell'' field
**Set ''InterpretMode'' → <tt>online free mode</tt>
**Make sure the ''DisplayResults'' box is checked
**Click on '''Edit Matrix''' next to ''TargetDefinitions'' and scroll to the bottom:
***In the first column replace <tt>9</tt> with <tt>BS</tt>
***In the second column replace <tt>9</tt> with <tt><BS></tt>
*Click '''Save Parameters''', and change the <tt>copy_spell</tt> portion of this parameter file name to <tt>free_spell</tt>
*This parameter file is now ready to use for that specific volunteer for future P300 spelling experiments

One program contributed to, and provided with, the BCI2000 system is the P300 GUI. This program utilizes MatLab R2007a to automatically sift through data files for datapoints indicative of a positive P300 reaction, and build a classifier matrix with those datapoints. The largest benefit to using this program is the obviated need for repeat configuration sessions, at the expense of decreased hands-on experience with the BCI2000 program itself. For a tutorial on how to perform the offline analysis with this GUI program, [[User Tutorial:P300 GUI for Offline Analysis Tutorial|please click here]].

To continue onto performing P300 spelling experiments, continue to [[User Tutorial:Performing a P300 Spelling Session|Performing a P300 Spelling Session]].

User Tutorial:Obtaining P300 Parameters in a Calibration Session

2009-02-05T19:02:22Z

Sbriskin: /* Analyzing The Calibration Session with Offline Analysis */

==Obtaining P300 Parameters in the Calibration Session==

Although the basic properties of the P300 evoked potential are the same for all individuals, the response's latency, width, and spatial pattern varies, and adaptation to individual parameters improves accuracy.

Thus, it is necessary to obtain these individual parameters prior to performing spelling experiments.

==Design of Calibration Session==

During the calibration session, the volunteer is asked to spell out a given word by using a P300 character matrix, pictured below. During each run, the volunteer is asked to focus on the next letter in the word they are spelling, as the rows and columns flash randomly and successively so that sometimes the flashing corresponds to the column or row containing the target character and sometimes it will not. As the volunteer counts the number of times the desired letter in the word flashes, a P300 response is generated. The purpose of the calibration session is to identify those features that discriminate between the desired and undesired rows/columns.

[[Image:P3SpellerMatrix.PNG|298px]]

After the first few runs are collected, an analysis tool will be used to generate a configuration file with weights that will determine what portions of the input data correspond to selecting the desired letter. These weights are applied to the configuration session for one more run, and then the data from this run is also analyzed with the MatLab tool, and the number of flashes needed to ensure 100% accuracy is determined. The second configuration file generated and applied. This final set of parameters are saved as that volunteer’s parameter file for future spelling sessions.

==Performing the Configuration Session==

*Start BCI2000 by running <tt>batch/P3Speller_<Your_Amplifier>.bat</tt>
*Press '''Config''', and load the baseline parameters for copy spelling that you made earlier.
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
*In the '''Application''' tab:
**Make sure that ''InterpretMode'' is set to <tt>copy mode</tt>, and ''DisplayResults'' (directly below InterpretMode) is unchecked
**Find the ''TextToSpell'' field. This should be set to ‘THE’, and you will be changing it after each run.
*Press '''Set Config''' to apply this configuration.

*Request that the volunteer sit in a relaxed position, and that the volunteer not move or speak during the runs.
*Turning off or dimming the lights can improve volunteer focus and performance.
*Showing the brain wave readouts to the volunteer can drive in the message of how artifact-generating behavior can be detrimental to the data.
*Press '''Start''' to show the flashing character matrix, and describe what the volunteer is expected to do.
*After you’ve explained the procedure, click '''Suspend''' to stop the process.
*Delete that run of data (The file should be named <tt>data\P300\<Volunteer's Initials>001R01.dat</tt>)

*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>QUICK</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>BROWN</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>FOX</tt>.
*Press '''Start''' to record the run.
*Once this recording has finished, close BCI2000 and locate the saved data files

==Analyzing The Calibration Session with Offline Analysis==
We will now perform an ‘Offline Analysis’ with a tool provided with the BCI2000 system.
[[Image:OfflineGUI.PNG|right]]
*Run <tt>tools/OfflineAnalysis/OfflineAnalysis.bat</tt>
*In the ''Analysis Domain'' field, choost '''Time (P300)'''
*In the ''Acquisition Type'' field, choose '''EEG'''
*Next to ''Spatial Filter'', choose '''None'''
*For '''Trial Change Condition''' enter <tt>states.StimulusBegin == 1</tt>
*For '''Target Condition 1''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 1)</tt>
*For '''Target Condition Label 1''' enter <tt>Attended Stimuli</tt>
**‘Attended Stimuli’ refers to the letter or character the person is counting the flashes of, and triggers when the correct stimulus is shown
*For '''Target Condition 2''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 0)</tt>
*For '''Target Condition Label 2''' enter <tt>Unattended Stimuli</tt>
**‘Unattended stimulus’ refers to the letters or characters the person is not counting the flashes of, and triggers when an incorrect stimulus is shown
*Click the '''Add''' button by ''Data Files''
*In this new dialog, select all of the data files taken during this configuration session, and click '''Open'''

*Click '''Generate Plots''' and wait for the feature plot to appear

{|
|height=160px|
|}

[[Image:FeaturesPlot.PNG|right|500px]]

*When this is complete, you will see a feature plot similar to the one to the right. The vertical axis corresponds to the locations while the horizontal corresponds to the time delay after the stimulus. The color coding shows the r-squared value of that datapoint, the darker the color indicating a higher predictability that it is produced by the desired letter being flashed.
*The darkest of these r-squared values between 250 and 550ms are what we are interested in. Pick the 4 points with the largest r-squared values between these times and record their time points and channels. The plot’s ''Data Cursor'' tool (Tools Menu → Data Cursor) allows for discrete identification of time points.

*With these four points, close the feature plot, and enter the channel numbers of the data points you found into the ''Waveform Channels'' field and the time points into the ''Topo Times'' field.
**In the example above, the four best data points have r-squared values <tt>0.02218</tt>, <tt>0.02179</tt>, <tt>0.01928</tt>, and <tt>0.019</tt>, occur at times <tt>388.7ms</tt>, <tt>392.6ms</tt>, <tt>384.8ms</tt>, and <tt>365.2ms</tt> respectively, and all four are detected by channel six. <tt>6, 6, 6, 6</tt> would be entered into ''Waveform Channels'' and <tt>388.7, 392.6, 384.8, 365.2</tt> would be entered into the ''Topo Times'' field. The order of the data points here does not matter, only that they are in the same order in both fields.
**If there are less than four points that seem appropriate, either they are at the wrong electrodes, at the wrong times, or simply have too low of an r-squared value, that is fine, three or two values can work, though the fewer values used the lower the accuracy will be.
**Set the ''Spatial Filter'' to '''Common Average Reference (CAR)'''
*Click '''Generate Plots''' to create the features plot again with a set of four graphs that show the correlation between the selected times after the desired stimuli is given (the red line) and the brain’s responses to when the desired stimuli is not given (the blue line).
**As seen below, the attended-stimuli reaction will typically be stronger than the unattended-stimuli, but in some cases the reverse is true. If the ‘unattended’ curve is larger than the ‘attended’ curve then make a note of this before moving on. The waveform seen here is similar to the others generated, only one is shown here for simplicity.
**Additionally before moving on, determine the location of the response seen. The P300 response is generally observed centered on the Cz electrode, or just behind and directly in between the ears, and does not involve the frontal regions of the brain. Assuming these characteristics are present, it is proper to proceed.

[[Image:Waveform.PNG|center|800px]]

*Now we will save these customizations to a volunteer-specific parameters file that will allow the volunteer to free-spell with very high accuracy.
*Start BCI2000 using <tt>batch/P3Speller_<Your_Amplifier>.bat</tt> file
*Click '''Config''', and load the <tt>P300_copy_speller_<Your_Amplifier>.prm</tt> made previously
*Under the '''Filtering''' tab, click the '''Edit Matrix''' button by ''Classifier'' near the bottom
*Change this matrix to have 4 columns and however many rows as values as you are using, and click '''Set New Matrix Size'''
**In the first column, labeled ''Input Channel'', enter the channel of the first value you use
**In the second column, labeled ''Input Element (bin)'', enter the time of the best classification, immediately followed with <tt>ms</tt>, as in <tt>388.7ms</tt>
**In the third column, enter 1 as the output channel
**In the fourth column, enter 1 if the ''Attended'' line was larger than the ''Unattended'' line, -1 if the ''unattended'' line was larger than the ''attended'' line
**Repeat these steps for the remaining rows
*Close this matrix, and click '''Save Parameters''' to save this file, naming it however you deem fit
*Use this new parameter file to repeat the configuration session a few times, adding new rows to the classifier matrix each time for the new data points to be utilized.
*When accuracy is reliably above 90%, click '''Config,''' and click on the '''Application''' tab:
**''NumberOfSequences'' to this number as well
**Delete the contents of the ''Text to Spell'' field
**Set ''InterpretMode'' → <tt>online free mode</tt>
**Make sure the ''DisplayResults'' box is checked
**Click on '''Edit Matrix''' next to ''TargetDefinitions'' and scroll to the bottom:
***In the first column replace <tt>9</tt> with <tt>BS</tt>
***In the second column replace <tt>9</tt> with <tt><BS></tt>
*Click '''Save Parameters''', and change the <tt>copy_spell</tt> portion of this parameter file name to <tt>free_spell</tt>
*This parameter file is now ready to use for that specific volunteer for future P300 spelling experiments

One program contributed to, and provided with, the BCI2000 system is the P300 GUI. This program utilizes MatLab R2007a to automatically sift through data files for datapoints indicative of a positive P300 reaction, and build a classifier matrix with those datapoints. The largest benefit to using this program is the obviated need for repeat configuration sessions, at the expense of decreased hands-on experience with the BCI2000 program itself. For a tutorial on how to perform the offline analysis with this GUI program, [[User Tutorial:P300 GUI for Offline Analysis Tutorial|please click here]].

To continue onto performing P300 spelling experiments, continue to [[User Tutorial:Performing a P300 Spelling Session|Performing a P300 Spelling Session]].

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-29T21:04:05Z

Sbriskin: /* Instructions to the Subject */

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

The next section is unnecessary for the gUSB amplifier. For more information regarding the gUSB amplifier, please consult the [[User_Reference:gUSBampADC|gUSB amplifier user reference.]]

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

==Configuring BCI2000==

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to the electrode positions according to the 10-20 convention
**This names the channels to the electrode positions they report
**In our example, <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt> has been entered
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to the electrode names entered into ''ChannelNames''

{|
|height="180px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Finally, save the parameter file where you deem appropriate.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying a message of "Right Hand", "Left Hand", "Both Hands", or "Both Feet".
*When "Left Hand" or "Right Hand" is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When "Both Hands" is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When "Both Feet" is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-29T20:57:10Z

Sbriskin: /* Configuring BCI2000 */

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

The next section is unnecessary for the gUSB amplifier. For more information regarding the gUSB amplifier, please consult the [[User_Reference:gUSBampADC|gUSB amplifier user reference.]]

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

==Configuring BCI2000==

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to the electrode positions according to the 10-20 convention
**This names the channels to the electrode positions they report
**In our example, <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt> has been entered
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to the electrode names entered into ''ChannelNames''

{|
|height="180px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Finally, save the parameter file where you deem appropriate.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-29T20:46:11Z

Sbriskin: /* Preparing for the Initial (Screening) Session */

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

The next section is unnecessary for the gUSB amplifier. For more information regarding the gUSB amplifier, please consult the [[User_Reference:gUSBampADC|gUSB amplifier user reference.]]

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

==Configuring BCI2000==

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="180px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Finally, save the parameter file where you deem appropriate.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T21:07:37Z

Sbriskin: /* Fine-Tuning the Classifier Matrix */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject.

Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically.

This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter each of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="120px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Performing Mu Rhythm Feedback Sessions==
Proper calibration of the Classifier and Spatial matrices are what take the most time. A Mu Rhythm Feedback Session should be performed with the classifier matrix to gauge the efficacy of the settings. In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using this configuration.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T21:07:11Z

Sbriskin:

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject.

Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically.

This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter each of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="120px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Fine-Tuning the Classifier Matrix==
Proper calibration of the Classifier and Spatial matrices are what take the most time. A Mu Rhythm Feedback Session should be performed with the classifier matrix to gauge the efficacy of the settings. In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using this configuration.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T20:44:17Z

Sbriskin: /* The Spatial Filter */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject.

Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically.

This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter each of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="120px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:59:25Z

Sbriskin: /* The Spatial Filter */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject.

Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically.

This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter each of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="90px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:59:11Z

Sbriskin: /* The Spatial Filter */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject.

Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically.

This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="90px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:58:46Z

Sbriskin:

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject.

Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically.

This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="80px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:58:24Z

Sbriskin: /* The Spatial Filter */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject.

Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically.

This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="60px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:57:59Z

Sbriskin: /* The Spatial Filter */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject. Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically. This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="100px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:57:48Z

Sbriskin: /* The Spatial Filter */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject. Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically. This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="80px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:57:38Z

Sbriskin: /* The Spatial Filter */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject. Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically. This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.
{|
|height="65px"|
|}

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

File:SpatialFilter.PNG

2009-01-28T19:56:00Z

Sbriskin:

User Tutorial:Configuring Online Feedback

2009-01-28T19:22:00Z

Sbriskin: /* The Classifier Matrix */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject. Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically. This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value <tt>2</tt>. This corresponds to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:20:14Z

Sbriskin: /* The Classifier Matrix */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject. Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically. This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
**If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:17:53Z

Sbriskin: /* The Classifier Matrix */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject. Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically. This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
*If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with <tt>Hz</tt>, as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T19:00:12Z

Sbriskin:

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Spatial Filter==
[[Image:SpatialFilter.PNG|right]]
The Spatial Filter artificially weighs the incoming data from the electrodes based on their placement on the scalp of the subject. Because we are targeting specific areas of the brain to monitor, the spatial filter allows the program to identify when the electrode of interest is activating specifically. This is done by subtracting the average of the surrounding electrodes' data from the electrode of interest. For example, as seen to the right the output channel <tt>C3_OUT</tt> is the data from <tt>C3</tt> minus one-quarter of <tt>F3</tt>, <tt>T7</tt>, <tt>Cz</tt>, and <tt>Pz</tt>.

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1. Click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter <tt>C3_OUT</tt> if the right hand are being used, <tt>C4_OUT</tt> for the left hand, or <tt>Cz_OUT</tt> for the feet.
*If both hands are being used, set ''Number of rows'' to 2, and click '''Set new matrix size'''. Enter <tt>C3_OUT</tt> under ''input channel'' in the first row, and <tt>C4_OUT</tt> in the second.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-28T17:46:22Z

Sbriskin: /* Subject-Specific Parameters */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject. Properly training the program and subject for high accuracy should not be expected during the first session, nor during the first few sessions.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab:
**Change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Change the ''SubjectName'' field to the subject's initials
**Make sure the ''SubjectSession'' field is set to <tt>001</tt> and ''SubjectRun'' is set to <tt>01</tt>

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1 (or the number of features that you wish to use); then, click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter the location of the desired location, e.g. <tt>CP3</tt> for the right hand. If you did not specify electrode locations when configuring your system, enter the channel number associated with the feedback electrode.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Repeat steps 2-5 for each additional feature moving down a row each time (i.e., enter the 2nd feature on the 2nd row, etc...).
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-27T19:38:38Z

Sbriskin: /* Starting up BCI2000 */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Multiple configuration sessions are typically needed to determine the proper classifier matrix for any one subject. Properly training the program and subject for high accuracy should not be expected during the first session, nor during the first few sessions.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
*Make sure the ''SubjectSession'' field is set to <tt>001</tt>

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1 (or the number of features that you wish to use); then, click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter the location of the desired location, e.g. <tt>CP3</tt> for the right hand. If you did not specify electrode locations when configuring your system, enter the channel number associated with the feedback electrode.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Repeat steps 2-5 for each additional feature moving down a row each time (i.e., enter the 2nd feature on the 2nd row, etc...).
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-27T19:18:58Z

Sbriskin:

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Properly training the program and subject for high accuracy should not be expected during the first session, nor during the first few sessions.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
*Make sure the ''SubjectSession'' field is set to <tt>001</tt>

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals. This is opened by selecting '''Edit Matrix''' next to the ''Classifier'' parameter in the '''Filtering''' tab.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1 (or the number of features that you wish to use); then, click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter the location of the desired location, e.g. <tt>CP3</tt> for the right hand. If you did not specify electrode locations when configuring your system, enter the channel number associated with the feedback electrode.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight. For further calibration, this weight can be increased to give stronger control or decreased to give finer control.
*Repeat steps 2-5 for each additional feature moving down a row each time (i.e., enter the 2nd feature on the 2nd row, etc...).
*Finally, save your configuration in a parameter file wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-27T19:14:13Z

Sbriskin:

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Properly training the program and subject for high accuracy should not be expected during the first session, nor during the first few sessions.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
*Make sure the ''SubjectSession'' field is set to <tt>001</tt>

==The Classifier Matrix==
The Classifier Matrix applies weights to the incoming data that allows the program to accurately identify Mu Rhythm signals.

They are entered into the ''Classifier'' parameter on the ''Filtering'' tab.
*There, click the ''Edit Matrix'' button associated with the ''Classifier'' parameter.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1 (or the number of features that you wish to use); then, click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter the location of the desired location, e.g. <tt>CP3</tt>. If you did not specify electrode locations when configuring your system, enter the channel number associated with the feedback electrode.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight.
*Repeat steps 2-5 for each additional feature moving down a row each time (i.e., enter the 2nd feature on the 2nd row, etc...).
*Finally, save your configuration to <tt>parms/subjects/mu_feedback/<Subject>002.prm</tt>, or wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-27T19:12:24Z

Sbriskin:

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Properly training the program and subject for high accuracy should not be expected during the first session, nor during the first few sessions.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
*Make sure the ''SubjectSession'' field is set to <tt>001</tt>

The Classifier Matrix

They are entered into the ''Classifier'' parameter on the ''Filtering'' tab.
*There, click the ''Edit Matrix'' button associated with the ''Classifier'' parameter.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1 (or the number of features that you wish to use); then, click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter the location of the desired location, e.g. <tt>CP3</tt>. If you did not specify electrode locations when configuring your system, enter the channel number associated with the feedback electrode.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight.
*Repeat steps 2-5 for each additional feature moving down a row each time (i.e., enter the 2nd feature on the 2nd row, etc...).
*Finally, save your configuration to <tt>parms/subjects/mu_feedback/<Subject>002.prm</tt>, or wherever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-27T18:06:42Z

Sbriskin: /* Subject-Specific Parameters */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Properly training the program and subject for high accuracy should not be expected during the first session, nor during the first few sessions.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
*Begin by loading the parameter file [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|saved previously]]
*In the '''Storage''' tab change the ''SourceData'' field to <tt>\task</tt> as opposed to <tt>\screening</tt>
**Make sure the ''SubjectSession'' field is set to <tt>001</tt>

==Configuring the Classifier==
Subject-specific electrode location and mu rhythm frequency are part of the classifier's configuration.
They are entered into the ''Classifier'' parameter on the ''Filtering'' tab.
*There, click the ''Edit Matrix'' button associated with the ''Classifier'' parameter.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1 (or the number of features that you wish to use); then, click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter the location of the desired location, e.g. <tt>CP3</tt>. If you did not specify electrode locations when configuring your system, enter the channel number associated with the feedback electrode.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight.
*Repeat steps 2-5 for each additional feature moving down a row each time (i.e., enter the 2nd feature on the 2nd row, etc...).
*Finally, save your configuration to <tt>parms/subjects/mu_feedback/<Subject>002.prm</tt>, or whereever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-27T17:34:18Z

Sbriskin: /* Configuring BCI2000 */

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

==Configuring BCI2000==

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="180px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Finally, save the parameter file where you deem appropriate.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Configuring Online Feedback

2009-01-26T21:04:16Z

Sbriskin: /* Starting up BCI2000 */

This tutorial step assumes that you have performed and [[User Tutorial:Analyzing the Initial Mu Rhythm Session|analyzed an initial session]].
Now you are going to create a subject-specific parameter configuration for on-line feedback.

==Starting up BCI2000==
Start BCI2000 using the appropriate batch file at <tt>batch/CursorTask_<YourAmplifier>.bat</tt>. You might consider creating a link to this file on the desktop.

This CursorTask will be used to configure the BCI2000 program for this specific subject. Properly training the program and subject for high accuracy should not be expected during the first session, nor during the first few sessions.

==Subject-Specific Parameters==
Now, we will construct a full parameter file that is specific to that subject:
{{LoadConfig|paramfile=parms/mu_tutorial/MuFeedback.prm}}
*Go to the ''Storage'' tab, and enter the subject's name or ID into the ''SubjectName'' parameter.
*Into the ''SubjectSession'' parameter, enter ''002''.
==Configuring the Classifier==
Subject-specific electrode location and mu rhythm frequency are part of the classifier's configuration.
They are entered into the ''Classifier'' parameter on the ''Filtering'' tab.
*There, click the ''Edit Matrix'' button associated with the ''Classifier'' parameter.
*Set ''Number of columns'' to 4, and ''Number of rows'' to 1 (or the number of features that you wish to use); then, click ''Set new matrix size'' to apply your changes.
*In the first column (of the first row), labeled ''input channel'', enter the location of the desired location, e.g. <tt>CP3</tt>. If you did not specify electrode locations when configuring your system, enter the channel number associated with the feedback electrode.
*In the second column, labeled ''input element (bin)'', enter feedback frequency in Hz, immediately followed with ''Hz'', as in <tt>12Hz</tt>.
*In the third column, enter the value 2 corresponding to the control channel for vertical control of the cursor.
*In the fourth column, enter 1 as the weight.
*Repeat steps 2-5 for each additional feature moving down a row each time (i.e., enter the 2nd feature on the 2nd row, etc...).
*Finally, save your configuration to <tt>parms/subjects/mu_feedback/<Subject>002.prm</tt>, or whereever you find appropriate.

==Next Step==
In the next step, you will learn how to actually [[User Tutorial:Performing a Mu Rhythm Feedback Session|perform a Mu rhythm feedback session]] using the configuration created in the present step.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]], [[User Reference:LinearClassifier]]

[[Category:Tutorial]]

User Tutorial:Analyzing the Initial Mu Rhythm Session

2009-01-26T21:01:34Z

Sbriskin: /* Picking Optimal Features */

This tutorial step assumes that you have [[User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session|performed an initial mu rhythm session]]. In order to identify the parameters (i.e., frequency and location) of a subject's mu rhythm, we will determine how different the EEG signal amplitude is for different types of imaginations that the subject performed during the recording. You will use the [[User Reference:BCI2000 Offline Analysis|BCI2000 Offline Analysis]] tool for this purpose.

==Generating a Feature Plot==
The first step in these analyses is to separate data into amplitudes at individual '''frequencies''' and '''locations.''' These amplitudes are called ''features,'' and their correspondence with the subject's imagination will be plotted as a so-called ''feature map.'' From a feature map, it is possible to determine those frequencies and locations whose amplitude is maximally correlated with the subject's task, i.e., those features that are most different between two conditions. These features will subsequently be used to provide feedback in a BCI experiment.

To generate a feature plot from the initial session's data, perform the following steps:
*Start the [[User Reference:BCI2000 Offline Analysis|BCI2000 Offline Analysis]] tool:
**If you have a version of Matlab installed, run <tt>tools/OfflineAnalysis/OfflineAnalysis.bat</tt>.
**Otherwise, follow the instructions provided [[User_Reference:BCI2000_Offline_Analysis#Systems_that_do_not_have_MATLAB_installed_or_with_versions_that_predate_MATLAB_v7.0|here]].
*In the '''Analysis Domain''' field, choose ''Frequency''.
*In the '''Acquisition Type''' field, choose "EEG".
*As a '''Spatial Filter''', choose "Common Average Reference (CAR)".
*Enter <tt>states.StimulusBegin == 1</tt> into the '''Trial Change Condition''' field.
*Into the field labeled '''Target Condition 1''', enter <tt>states.StimulusCode == 0</tt>.
*Enter the word "Rest" into the field labeled '''Target Condition Label 1'''.
*Similarly, enter <tt>states.StimulusCode == 2</tt> into the '''Target Condition 2''' field, and "Right Hand" into '''Target Condition Label 2'''.
*Click the "Add" button located besides the '''Data Files''' field. A file chooser dialog will appear; navigate to <tt>data/<Subject>001</tt>, and select all <tt>.dat</tt> files available there (use your keyboard's ''ctrl'' button to click-select multiple files), then click the dialog's "Open" button.
*Click "Generate Plots", and wait for the feature plot to appear.

Once the computation is complete, you will see a feature plot similar to the one below. In that plot, the horizontal axis corresponds to frequencies, and the vertical axis corresponds to individual channels. Color codes represent [[Glossary#r-squared|r-squared]] values, which are numbers between 0 and 1. R-squared values provide a measure for the amount to which a particular EEG feature (i.e., amplitude at a particular frequency and location) is influenced by the subject's task (e.g., hand vs. foot imagery).

[[Image:eeg1FeaturePlt.png|600px]]

Typically, there will be clusters of large r-squared values in the feature plot. The initial step to configure the online system is to determine which brain signal feature differed the most between two particular tasks. This is accomplished by picking the largest r-squared value from the map and by noting its corresponding frequency and location. However, it is important to verify whether the feature in question is consistent with the [[User_Tutorial:Introduction_to_the_Mu_Rhythm#Physical_Properties|mu rhythm's known properties]]. This verification is necessary to avoid misconfiguration due to [[User_Tutorial:EEG_Measurement_Setup#EEG_Artifacts|EEG artifacts]], other noise, or random effects.

==Generating Spectra and Topography Plots==
*Pick the four largest r-squared values from the feature map between 9 and 36Hz, and read off their frequencies and channels. The plot's "Data Cursor" tool (''Data Cursor'' from the ''Tools'' menu) may be helpful for this.
*In the analysis program's '''Spectra Channels''' field, enter the channel numbers you read off the feature map.
*In the '''Topo Frequencies''' field, enter the frequencies you read off the feature map.
*Click the '''Generate Plots''' button.

[[Image:MuRhythmModulation.PNG|600px]]

The generated '''topography plots''' display the spatial distribution of r-squared values. In this comparison of EEG activity for right-hand movements and rest, there should be a clear maximum of r-squared values over the ''left'' motor cortex as shown in subfigure (A) and (B) above.
The generated '''spectra plots''' display amplitude distributions, and r-squared measure, over frequencies. Ideally, they should appear similar to the (C) and (D) subfigures above.

==Analyzing Remaining Conditions==
Up to now, you performed an analysis of how brain activity is related to imagined movements of the '''Right Hand.''' In order to choose the most useful channel and frequency for online feedback, perform similar analyses for the remaining conditions:
*In the analysis program's '''Target Condition 2''' field, enter <tt>states.StimulusCode == 1</tt>, and ''Left Hand'' into '''Target Condition Label 2'''.
*Make sure the '''Overwrite existing plots''' check box is unchecked.
*Click ''Generate Plots'' to create a feature plot for imagined movement of the left hand.
*As previously, pick the four largest r-squared values, and compute spectra and topographies for their channels and frequencies.
*Results should somewhat resemble that derived for the right hand, except that the colored activity changes should appear over the right and not the left motor cortex.
*Repeat the analysis for conditions <tt>states.StimulusCode == 3</tt>: ''Both Hands'', and <tt>states.StimulusCode == 4</tt>: ''Both Feet''.
**For the ''both hands'' condition, the result should resemble a combination of ''left hand'' and ''right hand'' results.
**For ''both feet,'' modulated activity should be centered around electrode Cz.

==Picking Optimal Features==

Here we are looking for the frequency with the highest r-squared value and is between 9Hz and 36Hz. Further, acceptable readings are centered on the proper electrode for the predicted stimulus; C3 for readings from the right hand, C4 for the left hand, both C3 and C4 for both hands, and Cz for both feet.

Equipped with the frequency and electrode that provides the reading with the highest r-squared value, we can begin configuring the setup for proper use.

==Next Step==
[[User Tutorial:Configuring Online Feedback|Configuring Online Feedback]] shows you how to configure the BCI system using the parameters obtained in the analysis.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T19:55:05Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

==Configuring BCI2000==

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="180px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T19:54:54Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

==Configuring BCI2000==

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="180px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T19:40:05Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="180px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T19:39:31Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="200px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T19:39:10Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="230px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==
Each run gathers 20 data points that differentiate between moving the left hand, the right hand, both hands, and both feet. Ideally, there should be 100 points of data, meaning that five runs are suggested. This is done as five separate runs instead of one to allow the subject a chance between each run to rest, blink, swallow, speak, or have some water if so desired.

==Next Step==
Once all data points have been collected, the [[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] page shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T19:15:05Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircledMu.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="230px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==

==Next Step==
[[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

File:SourceTabCircledMu.PNG

2009-01-26T18:37:28Z

Sbriskin:

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T18:34:27Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircled.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''AlignChannels'' to <tt>0</tt>
**This makes sure that the electrodes, which gather data simultaneously, will be treated as such.
*''TransmitChList'' to <tt>F3 F4 T7 C3 Cz C4 T8 Pz</tt>

{|
|height="230px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 1 1</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==

==Next Step==
[[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T18:10:07Z

Sbriskin:

Although the [[User Tutorial:Introduction to the Mu Rhythm|basic properties of the Mu rhythm]] are identical for all humans, spatial patterns and exact frequencies are different across people. Thus, it is necessary to obtain these individual parameters prior to any feedback experiments, i.e., to calibrate the BCI system using data acquired from an initial session.

==Experimental Design==
In this initial session, the subject is instructed to imagine hand and/or foot movements in response to visual cues.
To identify a subject's Mu Rhythm, offline analyses then determine the frequency and location whose activity changes the most across conditions (e.g., hand imagery and rest). These analyses result in spectra calculated at different locations or in topographical plots at particular frequencies.

==Preparing for the Initial (Screening) Session==
To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, (which displays the instructions to the subject), is on the right of Monitor 1, (the experimenter's screen), and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

*Start BCI2000 by double-clicking <tt>batch/StimulusPresentation_screening_<YourAmplifier>.bat</tt>.
**For repeated use, placing a link to this file on the desktop might be a good idea.
*In the operator module, click '''Config'''.
*Click '''Load Parameters''', and load <tt>parameters/fragments/parms/fragment_<YourAmplifier>.prm</tt> and <tt>parameters/fragments/parms/Mu_screening_left_vs_right.prm</tt>
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
**Please note that the data will be stored into a directory dedicated to the screening data.

[[Image:SourceTabCircled.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>F3 F4 T9 C3 Cz C4 T8 Pz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''TransmitChList'' to <tt>F3 F4 T9 C3 Cz C4 T8 Pz</tt>

{|
|height="230px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor
*Take note of the ''Sequence'' field:
**This field calls for four single-digit numbers separated by one space. Initially it will be <tt>1 1 0 0</tt>.
**These numbers are the relative frequency of calling for the different subject activity. The first number corresponds to the left hand, the second is the right hand, the third for both hands, the fourth for both feet.
**Therefore, setting this field to <tt>2 1 0 1</tt> will call for the subject to move the left hand twice as often as the right or both feet, and will never call for both hands to be moved.
*Also note the ''UserComment'' field. This should read <tt>Left Hand vs. Right Hand</tt>
*Click '''Set Config'''.
*Instruct the subject as described in the section below.

==Instructions to the Subject==
During the initial session, the subject's screen will either be blank, or displaying an arrow pointing up, down, left or right.
*When a left or right arrow is displayed, imagine movement of the respective hand. The imagined movement should be continuous opening and closing of the hand (e.g., squeezing a tennis ball) at a rate of about one opening/closing per second.
*When an up arrow is displayed, imagine simultaneous movement of both hands. This should be the same kind of movement as described for a single hand.
*When a down arrow is displayed, imagine movements of both feet. The movement should be similar to the one described for hands, i.e., imagine opening and closing your feet as if you could use them to grip an object.
*When you see a blank screen, please relax and stop any movement imagery.

==Performing the Initial Session==

==Next Step==
[[User Tutorial:Analyzing the Initial Mu Rhythm Session|Analyzing the Initial Mu Rhythm Session]] shows you how to analyze data from the initial session in order to determine parameters for online feedback.

==See also==
[[User Tutorial:Mu Rhythm BCI Tutorial]]

[[Category:Tutorial]]

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T17:08:30Z

Sbriskin: /* Performing the Initial Session */

User Tutorial:Obtaining Mu Rhythm Parameters in an Initial Session

2009-01-26T17:01:50Z

Sbriskin:

User Tutorial:Preparing your BCI2000 system for P300 BCI experiments for the first time

2009-01-26T16:56:01Z

Sbriskin:

To begin, you first need to gather some system data. For setup, this tutorial will assume that you will be using a dual-monitor setup as shown below, with the administrator of the sessions operating on monitor 1, and the volunteer will be watching monitor 2.

Open Display Properties by right-clicking on an empty portion of the desktop and clicking '''Properties''', and navigating to the '''Settings''' tab.

{|
|colspan="2" align="center"|[[Image:MonitorSetupOrientation.PNG|center]]
|-
|colspan="2" align="center"|Make sure that Monitor 2, which displays the character matrix, is on the right of Monitor 1, the experimenter's screen, and is aligned along the top.
|-
|colspan="2" align="center" height=50px|
|-
|align="center"|[[Image:MonitorSetupPrimaryRes.PNG|center]]
|[[Image:MonitorSetupSecondaryRes.PNG|center]]
|-
|colspan="2" align="center"|What we need to make a note of is the '''width''' of monitor 1, and the '''width and height''' of monitor 2.
|-
|colspan="2" align="center"|In this example we see that Monitor 1 is 2048 pixels wide, and monitor 2 is 1024 pixels wide by 768 pixels tall.
|-
|colspan="2" align="center" height=50px|
|}

Plug your adapter into the computer and turn it on.

Now we will determine what port your amplifier is connected to by going through '''Start → Settings → Control Panel → System → Hardware → Device Manager → Ports (COM & LPT)'''. In this example, we see that the amplifier is connected to ''COM8''.

<gallery caption="Finding the COM port" widths="300px" heights="300px" perrow="4">
Image:ControlPanel-Circled.PNG|Accessing the Control Panel
Image:System.PNG|Opening the System Properties
Image:DeviceManager.PNG|Opening the Device Manager
Image:COMCircled.PNG|Identifying the COM Port
</gallery>

You can now close all open windows.

Run the <tt>batch\P3Speller_<your amplifier>.bat</tt> file. In this example, we are using the gMOBIlab amplifier, so we would run <tt>batch\P3Speller_gMOBIlab.bat</tt>

Press the '''Config''' button to bring up the BCI2000 Configuration window

Press '''Load Parameters''' and load <tt>parameters\<your_amplifier>\parms\P300_copy_speller.prm</tt>

In the '''Visualize''' tab, set ''VisualizeSourceTime'' (Third line under ‘Source Signal’) to 6
*This sets the window showing the brain waves to show 6 seconds of information at a time

[[Image:SourceTabCircled.PNG|right|500px]]

In the '''Source''' tab, set:
*''SourceCh'' to <tt>8</tt>
**This tells the program that only 8 channels will be sending data
*''ChannelNames'' to <tt>Fz Cz P3 Pz P4 PO3 PO4 Oz</tt>
**This names the channels to the electrode positions they report
*''SourceChOffset'' to <tt>0 0 0 0 0 0 0 0</tt>
**This tells the program that none of the channels have an offset
*''SourceChGain'' to <tt>0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019</tt>
**This is the conversion for each channel for A/D information into microvolts
*''COM port'' to the port name that you found earlier
**In this example we would put in <tt>COM8:</tt>
*''NotchFilter'' to <tt>disable</tt>
**We will use this to identify any poorly-gelled electrodes
*''TransmitChList'' to <tt>Fz Cz P3 Pz P4 PO3 PO4 Oz</tt>
**This will label the graph with which electrode is which display

{|
|height="230px"|
|}

In the '''Application''' tab, set:
*''WindowWidth'' to the width of the display monitor, found earlier
*''WindowHeight'' to the height of the display monitor, found earlier
**In our example, ''WindowWidth'' would be set to <tt>1024</tt> and ''WindowHeight'' would be set to <tt>768</tt>
*''WindowLeft'' to the width of the experimenter’s monitor plus one
**In our example, this would be <tt>2049</tt>
**This shifts the character display to the right 2049 pixels, making it display on the secondary monitor

Press ''‘Save Parameters''’, and save to a file named <tt>P300_copy_speller_<Your_Amplifier>.prm</tt>, or however you deem fit

This file is now the base parameter file for your amplifier when configuring the system to user-specific settings.

To continue this tutorial, please continue to [[User Tutorial:EEG Measurement Setup|EEG Measurement Setup]]

User Tutorial:Obtaining P300 Parameters in a Calibration Session

2009-01-15T20:52:54Z

Sbriskin:

==Obtaining P300 Parameters in the Calibration Session==

Although the basic properties of the P300 evoked potential are the same for all individuals, the response's latency, width, and spatial pattern varies, and adaptation to individual parameters improves accuracy.

Thus, it is necessary to obtain these individual parameters prior to performing spelling experiments.

==Design of Calibration Session==

During the calibration session, the volunteer is asked to spell out a given word by using a P300 character matrix, pictured below. During each run, the volunteer is asked to focus on the next letter in the word they are spelling, as the rows and columns flash randomly and successively so that sometimes the flashing corresponds to the column or row containing the target character and sometimes it will not. As the volunteer counts the number of times the desired letter in the word flashes, a P300 response is generated. The purpose of the calibration session is to identify those features that discriminate between the desired and undesired rows/columns.

[[Image:P3SpellerMatrix.PNG|298px]]

After the first few runs are collected, an analysis tool will be used to generate a configuration file with weights that will determine what portions of the input data correspond to selecting the desired letter. These weights are applied to the configuration session for one more run, and then the data from this run is also analyzed with the MatLab tool, and the number of flashes needed to ensure 100% accuracy is determined. The second configuration file generated and applied. This final set of parameters are saved as that volunteer’s parameter file for future spelling sessions.

==Performing the Configuration Session==

*Start BCI2000 by running <tt>batch/P3Speller_<Your_Amplifier>.bat</tt>
*Press '''Config''', and load the baseline parameters for copy spelling that you made earlier.
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
*In the '''Application''' tab:
**Make sure that ''InterpretMode'' is set to <tt>copy mode</tt>, and ''DisplayResults'' (directly below InterpretMode) is unchecked
**Find the ''TextToSpell'' field. This should be set to ‘THE’, and you will be changing it after each run.
*Press '''Set Config''' to apply this configuration.

*Request that the volunteer sit in a relaxed position, and that the volunteer not move or speak during the runs.
*Turning off or dimming the lights can improve volunteer focus and performance.
*Showing the brain wave readouts to the volunteer can drive in the message of how artifact-generating behavior can be detrimental to the data.
*Press '''Start''' to show the flashing character matrix, and describe what the volunteer is expected to do.
*After you’ve explained the procedure, click '''Suspend''' to stop the process.
*Delete that run of data (The file should be named <tt>data\P300\<Volunteer's Initials>001R01.dat</tt>)

*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>QUICK</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>BROWN</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>FOX</tt>.
*Press '''Start''' to record the run.
*Once this recording has finished, close BCI2000 and locate the saved data files

==Analyzing The Calibration Session with Offline Analysis==
We will now perform an ‘Offline Analysis’ with a tool provided with the BCI2000 system.
[[Image:OfflineGUI.PNG|right]]
*Run <tt>tools/OfflineAnalysis/OfflineAnalysis.bat</tt>
*In the ''Analysis Domain'' field, choost '''Time (P300)'''
*In the ''Acquisition Type'' field, choose '''EEG'''
*Next to ''Spatial Filter'', choose '''None'''
*For '''Trial Change Condition''' enter <tt>states.StimulusBegin == 1</tt>
*For '''Target Condition 1''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 1)</tt>
*For '''Target Condition Label 1''' enter <tt>Attended Stimuli</tt>
**‘Attended Stimuli’ refers to the letter or character the person is counting the flashes of, and triggers when the correct stimulus is shown
*For '''Target Condition 2''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 0)</tt>
*For '''Target Condition Label 2''' enter <tt>Unattended Stimuli</tt>
**‘Unattended stimulus’ refers to the letters or characters the person is not counting the flashes of, and triggers when an incorrect stimulus is shown
*Click the '''Add''' button by ''Data Files''
*In this new dialog, select all of the data files taken during this configuration session, and click '''Open'''

*Click '''Generate Plots''' and wait for the feature plot to appear

{|
|height=160px|
|}

[[Image:FeaturesPlot.PNG|right|500px]]

*When this is complete, you will see a feature plot similar to the one to the right. The vertical axis corresponds to the locations while the horizontal corresponds to the time delay after the stimulus. The color coding shows the r-squared value of that datapoint, the darker the color indicating a higher predictability that it is produced by the desired letter being flashed.
*The darkest of these r-squared values between 250 and 550ms are what we are interested in. Pick the 4 points with the largest r-squared values between these times and record their time points and channels. The plot’s ''Data Cursor'' tool (Tools Menu → Data Cursor) allows for discrete identification of time points.

*With these four points, close the feature plot, and enter the channel numbers of the data points you found into the ''Waveform Channels'' field and the time points into the ''Topo Times'' field.
**In the example above, the four best data points have r-squared values <tt>0.02218</tt>, <tt>0.02179</tt>, <tt>0.01928</tt>, and <tt>0.019</tt>, occur at times <tt>388.7ms</tt>, <tt>392.6ms</tt>, <tt>384.8ms</tt>, and <tt>365.2ms</tt> respectively, and all four are detected by channel six. <tt>6, 6, 6, 6</tt> would be entered into ''Waveform Channels'' and <tt>388.7, 392.6, 384.8, 365.2</tt> would be entered into the ''Topo Times'' field. The order of the data points here does not matter, only that they are in the same order in both fields.
**If there are less than four points that seem appropriate, either they are at the wrong electrodes, at the wrong times, or simply have too low of an r-squared value, that is fine, three or two values can work, though the fewer values used the lower the accuracy will be.
*Click '''Generate Plots''' to create the features plot again with a set of four graphs that show the correlation between the selected times after the desired stimuli is given (the red line) and the brain’s responses to when the desired stimuli is not given (the blue line).
**As seen below, the attended-stimuli reaction will typically be stronger than the unattended-stimuli, but in some cases the reverse is true. If the ‘unattended’ curve is larger than the ‘attended’ curve then make a note of this before moving on. The waveform seen here is similar to the others generated, only one is shown here for simplicity.
**Additionally before moving on, determine the location of the response seen. The P300 response is generally observed centered on the Cz electrode, or just behind and directly in between the ears, and does not involve the frontal regions of the brain. Assuming these characteristics are present, it is proper to proceed.

[[Image:Waveform.PNG|center|800px]]

*Now we will save these customizations to a volunteer-specific parameters file that will allow the volunteer to free-spell with very high accuracy.
*Start BCI2000 using <tt>batch/P3Speller_<Your_Amplifier>.bat</tt> file
*Click '''Config''', and load the <tt>P300_copy_speller_<Your_Amplifier>.prm</tt> made previously
*Under the '''Filtering''' tab, click the '''Edit Matrix''' button by ''Classifier'' near the bottom
*Change this matrix to have 4 columns and however many rows as values as you are using, and click '''Set New Matrix Size'''
**In the first column, labeled ''Input Channel'', enter the channel of the first value you use
**In the second column, labeled ''Input Element (bin)'', enter the time of the best classification, immediately followed with <tt>ms</tt>, as in <tt>388.7ms</tt>
**In the third column, enter 1 as the output channel
**In the fourth column, enter 1 if the ''Attended'' line was larger than the ''Unattended'' line, -1 if the ''unattended'' line was larger than the ''attended'' line
**Repeat these steps for the remaining rows
*Close this matrix, and click '''Save Parameters''' to save this file, naming it however you deem fit
*Use this new parameter file to repeat the configuration session a few times, adding new rows to the classifier matrix each time for the new data points to be utilized.
*When accuracy is reliably above 90%, click '''Config,''' and click on the '''Application''' tab:
**''NumberOfSequences'' to this number as well
**Delete the contents of the ''Text to Spell'' field
**Set ''InterpretMode'' → <tt>online free mode</tt>
**Make sure the ''DisplayResults'' box is checked
**Click on '''Edit Matrix''' next to ''TargetDefinitions'' and scroll to the bottom:
***In the first column replace <tt>9</tt> with <tt>BS</tt>
***In the second column replace <tt>9</tt> with <tt><BS></tt>
*Click '''Save Parameters''', and change the <tt>copy_spell</tt> portion of this parameter file name to <tt>free_spell</tt>
*This parameter file is now ready to use for that specific volunteer for future P300 spelling experiments

One program contributed to, and provided with, the BCI2000 system is the P300 GUI. This program utilizes MatLab R2007a to automatically sift through data files for datapoints indicative of a positive P300 reaction, and build a classifier matrix with those datapoints. The largest benefit to using this program is the obviated need for repeat configuration sessions, at the expense of decreased hands-on experience with the BCI2000 program itself. For a tutorial on how to perform the offline analysis with this GUI program, [[User Tutorial:P300 GUI for Offline Analysis Tutorial|please click here]].

To continue onto performing P300 spelling experiments, continue to [[User Tutorial:Performing a P300 Spelling Session|Performing a P300 Spelling Session]].

User Tutorial:Obtaining P300 Parameters in a Calibration Session

2009-01-15T02:33:18Z

Sbriskin: /* Analyzing The Calibration Session with Offline Analysis */

==Obtaining P300 Parameters in the Calibration Session==

Although the basic properties of the P300 evoked potential are the same for all individuals, the response's latency, width, and spatial pattern varies, and adaptation to individual parameters improves accuracy.

Thus, it is necessary to obtain these individual parameters prior to performing spelling experiments.

==Design of Calibration Session==

During the calibration session, the volunteer is asked to spell out a given word by using a P300 character matrix, pictured below. During each run, the volunteer is asked to focus on the next letter in the word they are spelling, as the rows and columns flash randomly and successively so that sometimes the flashing corresponds to the column or row containing the target character and sometimes it will not. As the volunteer counts the number of times the desired letter in the word flashes, a P300 response is generated. The purpose of the calibration session is to identify those features that discriminate between the desired and undesired rows/columns.

[[Image:P3SpellerMatrix.PNG|298px]]

After the first few runs are collected, an analysis tool will be used to generate a configuration file with weights that will determine what portions of the input data correspond to selecting the desired letter. These weights are applied to the configuration session for one more run, and then the data from this run is also analyzed with the MatLab tool, and the number of flashes needed to ensure 100% accuracy is determined. The second configuration file generated and applied. This final set of parameters are saved as that volunteer’s parameter file for future spelling sessions.

==Performing the Configuration Session==

*Start BCI2000 by running <tt>batch/P3Speller_<Your_Amplifier>.bat</tt>
*Press '''Config''', and load the baseline parameters for copy spelling that you made earlier.
*In the '''Storage''' tab:
**Set ''SubjectName'' to the volunteer’s initials.
**Set ''SubjectSession'' to <tt>001</tt>
**Set ''SubjectRun'' to <tt>01</tt>
*In the '''Application''' tab:
**Make sure that ''InterpretMode'' is set to <tt>copy mode</tt>, and ''DisplayResults'' (directly below InterpretMode) is unchecked
**Find the ''TextToSpell'' field. This should be set to ‘THE’, and you will be changing it after each run.
*Press '''Set Config''' to apply this configuration.

*Request that the volunteer sit in a relaxed position, and that the volunteer not move or speak during the runs.
*Turning off or dimming the lights can improve volunteer focus and performance.
*Showing the brain wave readouts to the volunteer can drive in the message of how artifact-generating behavior can be detrimental to the data.
*Press '''Start''' to show the flashing character matrix, and describe what the volunteer is expected to do.
*After you’ve explained the procedure, click '''Suspend''' to stop the process.
*Delete that run of data (The file should be named <tt>data\P300\<Volunteer's Initials>001R01.dat</tt>)

*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>QUICK</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>BROWN</tt>.
*Press '''Start''' to record the run.
*Once it has finished automatically, click '''Config''' and change the ''TextToSpell'' in the '''Application''' tab to <tt>FOX</tt>.
*Press '''Start''' to record the run.
*Once this recording has finished, close BCI2000 and locate the saved data files

==Analyzing The Calibration Session with Offline Analysis==
We will now perform an ‘Offline Analysis’ with a tool provided with the BCI2000 system.
[[Image:OfflineGUI.PNG|right]]
*Run <tt>tools/OfflineAnalysis/OfflineAnalysis.bat</tt>
*In the ''Analysis Domain'' field, choost '''Time (P300)'''
*In the ''Acquisition Type'' field, choose '''EEG'''
*Next to ''Spatial Filter'', choose '''None'''
*For '''Trial Change Condition''' enter <tt>states.StimulusBegin == 1</tt>
*For '''Target Condition 1''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 1)</tt>
*For '''Target Condition Label 1''' enter <tt>Attended Stimuli</tt>
**‘Attended Stimuli’ refers to the letter or character the person is counting the flashes of, and triggers when the correct stimulus is shown
*For '''Target Condition 2''' enter <tt>(states.StimulusCode > 0) & (states.StimulusType == 0)</tt>
*For '''Target Condition Label 2''' enter <tt>Unattended Stimuli</tt>
**‘Unattended stimulus’ refers to the letters or characters the person is not counting the flashes of, and triggers when an incorrect stimulus is shown
*Click the '''Add''' button by ''Data Files''
*In this new dialog, select all of the data files taken during this configuration session, and click '''Open'''

*Click '''Generate Plots''' and wait for the feature plot to appear

{|
|height=160px|
|}

[[Image:FeaturesPlot.PNG|right|500px]]

*When this is complete, you will see a feature plot similar to the one to the right. The vertical axis corresponds to the locations while the horizontal corresponds to the time delay after the stimulus. The color coding shows the r-squared value of that datapoint, the darker the color indicating a higher predictability that it is produced by the desired letter being flashed.
*The darkest of these r-squared values between 250 and 550ms are what we are interested in. Pick the 4 points with the largest r-squared values between these times and record their time points and channels. The plot’s ''Data Cursor'' tool (Tools Menu → Data Cursor) allows for discrete identification of time points.

*With these four points, close the feature plot, and enter the channel numbers of the data points you found into the ''Waveform Channels'' field and the time points into the ''Topo Times'' field.
**In the example above, the four best data points have r-squared values <tt>0.02218</tt>, <tt>0.02179</tt>, <tt>0.01928</tt>, and <tt>0.019</tt>, occur at times <tt>388.7ms</tt>, <tt>392.6ms</tt>, <tt>384.8ms</tt>, and <tt>365.2ms</tt> respectively, and all four are detected by channel six. <tt>6, 6, 6, 6</tt> would be entered into ''Waveform Channels'' and <tt>388.7, 392.6, 384.8, 365.2</tt> would be entered into the ''Topo Times'' field. The order of the data points here does not matter, only that they are in the same order in both fields.
**If there are less than four points that seem appropriate, either they are at the wrong electrodes, at the wrong times, or simply have too low of an r-squared value, that is fine, three or two values can work, though the fewer values used the lower the accuracy will be.
*Click '''Generate Plots''' to create the features plot again with a set of four graphs that show the correlation between the selected times after the desired stimuli is given (the red line) and the brain’s responses to when the desired stimuli is not given (the blue line).
**As seen below, the attended-stimuli reaction will typically be stronger than the unattended-stimuli, but in some cases the reverse is true. If the ‘unattended’ curve is larger than the ‘attended’ curve then make a note of this before moving on. The waveform seen here is similar to the others generated, only one is shown here for simplicity.
**Additionally before moving on, determine the location of the response seen. The P300 response is generally observed centered on the Cz electrode, or just behind and directly in between the ears, and does not involve the frontal regions of the brain. Assuming these characteristics are present, it is proper to proceed.

[[Image:Waveform.PNG|center|800px]]

*Now we will save these customizations to a volunteer-specific parameters file that will allow the volunteer to free-spell with very high accuracy.
*Start BCI2000 using <tt>batch/P3Speller_<Your_Amplifier>.bat</tt> file
*Click '''Config''', and load the <tt>P300_copy_speller_<Your_Amplifier>.prm</tt> made previously
*Under the '''Filtering''' tab, click the '''Edit Matrix''' button by ''Classifier'' near the bottom
*Change this matrix to have 4 columns and however many rows as values as you are using, and click '''Set New Matrix Size'''
**In the first column, labeled ''Input Channel'', enter the channel of the first value you use
**In the second column, labeled ''Input Element (bin)'', enter the time of the best classification, immediately followed with <tt>ms</tt>, as in <tt>388.7ms</tt>
**In the third column, enter 1 as the output channel
**In the fourth column, enter 1 if the ''Attended'' line was larger than the ''Unattended'' line, -1 if the ''unattended'' line was larger than the ''attended'' line
**Repeat these steps for the remaining rows
*Close this matrix, and click '''Save Parameters''' to save this file, naming it however you deem fit
*Use this new parameter file to repeat the configuration session a few times, adding new rows to the classifier matrix each time for the new data points to be utilized.
*When accuracy is reliably above 90%, click '''Config,''' and click on the '''Application''' tab:
**''NumberOfSequences'' to this number as well
**Delete the contents of the ''Text to Spell'' field
**Set ''InterpretMode'' → <tt>online free mode</tt>
**Make sure the ''DisplayResults'' box is checked
**Click on '''Target Matrix,''' and scroll to the bottom:
***In the first column replace <tt>9</tt> with <tt>BS</tt>
***In the second column replace <tt>9</tt> with <tt><BS></tt>
*Click '''Save Parameters''', and change the <tt>copy_spell</tt> portion of this parameter file name to <tt>free_spell</tt>
*This parameter file is now ready to use for that specific volunteer for future P300 spelling experiments

One program contributed to, and provided with, the BCI2000 system is the P300 GUI. This program utilizes MatLab R2007a to automatically sift through data files for datapoints indicative of a positive P300 reaction, and build a classifier matrix with those datapoints. The largest benefit to using this program is the obviated need for repeat configuration sessions, at the expense of decreased hands-on experience with the BCI2000 program itself. For a tutorial on how to perform the offline analysis with this GUI program, [[User Tutorial:P300 GUI for Offline Analysis Tutorial|please click here]].

To continue onto performing P300 spelling experiments, continue to [[User Tutorial:Performing a P300 Spelling Session|Performing a P300 Spelling Session]].

User Tutorial:P300 GUI for Offline Analysis Tutorial

2009-01-15T02:31:36Z

Sbriskin:

[[Image:P300_GUI.PNG|right|700x600px]]

*Run <tt>src/contrib./Tools/P300GUI/P300_GUI.m</tt>
*Close the Editor window that appears, and type in <tt>P300_GUI</tt>
*A GUI such as the one to the right will appear
*In the upper left-hand column, click on '''Select Training Data'''
*Select the data files collected from the runs for <tt>THE</tt>, <tt>QUICK</tt>, <tt>BROWN</tt>, and <tt>FOX</tt>
*In the lower left-hand column, press the '''Generate Feature Weights''' button
*A pair of graphs should be displayed, similar to those below.
**The important feature is to notice that the red line in the upper graph, the accuracy, increases with more flashes
*Close the window of the graphs
*In the lower-middle column, type <tt>Training</tt> for ''File Prefix:'', and <tt>1</tt> for ''File #s to Save''
*Click on '''*.prm (v2)'''
**This will generate a <tt>.prm</tt> (parameter) file in the same folder as the data files

{|
|height=200px|
|}

*Returning to BCI2000, click on '''Config''' and '''Load Parameters''', selecting the new <tt>.prm</tt> file
*In the '''Application''' tab, change ''TextToSpell'' to <tt>JUMPS</tt>, and click '''Set Config'''
*Press '''Start''' to collect one more run of data
*Once the last run of data has finished, go back to the P300_GUI program and click '''Add Test Data''' in the upper right-hand column
*Select the newest set of data, and click '''Apply Feature Weights''' in the lower right-hand column - A new window of graphs will appear
*The important thing to see from these graphs is where the red line in the upper graph reaches 100%.
**In the example below, this is from 9 to 11, and then drops. This means that 10 flashes gives the best accuracy.

[[Image:9to11.PNG|center]]

*If this does not give an acceptable level of accuracy, (typically 95% or higher), repeat more test runs to collect more data files. Add these data files as above, and click '''Apply Feature Weights''' in the lower right-hand column to look for how many flashes is needed. Repeat as necessary.
*In the lower middle column, type <tt>Trained</tt> for ''File Prefix'' and <tt>1</tt> for ''File #s to Save''
*Click '''*.prm (v2)''' again to generate a <tt>Trained</tt> parameter file
*Close BCI2000, MatLab, and the P300_GUI programs
*Open BCI2000 with the batch\P3Speller_<Your_Amplifier>.bat file
*Click on '''Config''', and '''Load Parameters'''
*Load the base parameter file for your amplifier, and then the <tt>Trained</tt> parameter file.
*In the '''Filtering''' tab, set ''EpochsToAverage'' to the ideal number found by how many flashes were needed to attain maximum accuracy. In our example, this would be 10.
*In the '''Application''' tab:
**''NumberOfSequences'' to this number as well
**Delete the contents of the ''Text to Spell'' field
**Set ''InterpretMode'' → <tt>online free mode</tt>
**Make sure the ''DisplayResults'' box is checked
**Click on '''Target Matrix,''' and scroll to the bottom:
***In the first column replace <tt>9</tt> with <tt>BS</tt>
***In the second column replace <tt>9</tt> with <tt><BS></tt>
*Click '''Save Parameters''' and name as you see fit. Typically this parameter file would be <tt>P300_<Your Amplifier>_<Volunteer's Intials>_Free_Spelling.prm</tt> This file is now the file used for that volunteer in later P300 Spelling sessions

For full details regarding the P300 GUI, the User's Guide is available [[P300_GUI_Guide|here.]]

To continue onto performing P300 spelling experiments, continue to [[User Tutorial:Performing a P300 Spelling Session|Performing a P300 Spelling Session]].

P300 GUI Guide

2009-01-14T20:14:39Z

Sbriskin: /* Release Notes */

==Introduction==

The P300 GUI (Graphical User Interface) can be used to train and test a linear classifier for detection of evoked potentials collected with BCI2000[6] and visualize the waveforms and classification results. It is designed for analysis of BCI2000 data collected using the P3Speller or P3AV paradigms. The program generates feature weights derived via various regression techniques. The specifics of the feature space and training routine can be manipulated using the GUI. The feature weights derived by the GUI can be saved and imported into BCI2000 as a parameter file fragment for online testing.

The GUI is capable of performing several functions independently:

*Classifier Training: generates feature weights from BCI2000 P3Speller or P3AV data files (classifier weights can be saved and imported into BCI2000)
*Classifier Testing: applies current or previously created feature weights to BCI2000 P3Speller or P3AV data files and compares the results
*Visualizing evoked potentials from BCI2000 P3Speller or P3AV data files

The details and operation of each of these functions are described subsequently.

==Using the GUI==
===Getting Started===

====System Requirements====
*MatLab 7 with the Statistics Toolbox and 1 GB of RAM is recommended

====To Launch GUI====
#Open Matlab and select the path containing the P300_GUI code.
#:[[Image:Launch_GUI.png]]
#From the Matlab command window, type P300_GUI and press Enter. The P300_GUI control panel will appear.

====The GUI Control Panel====
[[Image:GUI_Control_Panel.png|frame|center|<div align="center">Figure 1: The GUI Control Panel [#'s] correspond to subsequent descriptions of function</div>]]

'''''Training Data Pane'''''
{|
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|[1]||Select Training Data Button Use this button to load BCI2000 data files for training. The information for the selected files will appear below the button.
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|[2]||Regression Method Selects the regression method used to derive feature weights. Select from SWLDA (Stepwise Linear Discriminant Analysis), Ordinary Least Squares Regression, and Logistic Regression.
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|[3]||Resp. Window Used to specify the begin and end time points in milliseconds (ms) following the stimuli collected for the analysis. This automatically converted into samples according to the sampling rate of the data (rounded). Only a single data window can be entered and will be evaluated. Negative values are accepted and begin < end.
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|[4]||% random sample Used to train on a sub-sample of the responses randomly selected from the training data. The default (100%) uses all of the data for training.
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|[5]||Spatial Filter Pull-Down Menu Selects the spatial filter applied to the training data. Select 'RAW & CAR', 'RAW only', or 'CAR only' from the drop-down menu. RAW is no spatial filter applied to the data and CAR is a common average reference filter using all of the channels contained in the data file (not just the channels specified in the GUI channel set!!!). The RAW & CAR option will evaluate both types simultaneously for comparison purposes.
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|[6]||Decimation Frequencies Used to specify the temporal decimation frequency of the data in Hz. Multiple values can be entered for simultaneous evaluation and comparison. Set to the sampling rate for no decimation. The lower the Decimation Frequency, the less original data retained for processing.
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|[7]||Max model features Used to specify the maximum number of features to be kept in the SWLDA model (has no effect on the LG or LS regression options). Only a single value can be entered for evaluation.
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|[8]||Channel Set 1 Used to specify the primary channel set that will be used to create feature weights. The specified channels must be a subset of the channels contained in the training data file.
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|[9]||Channel Set 2 Used to specify a secondary channel set to evaluate simultaneously for comparison purposes. The specified channels must be a subset of the channels contained in the training data file. Can be enabled by clicking the check-box.
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|[10]||Create Spreadsheet enable Used to create a Microsoft Excel spreadsheet that summarizes the classification results. Can be enabled by clicking the check-box. If enabled, a spreadsheet having the same name as the training set will be created in the directory of the training files. The results of the training classification will be located in the worksheet having the same name of the training set within the file. If no training file is used, the spreadsheet will use the name and location of the first test file. The results in the spreadsheet will be overwritten if an analysis is performed again using data from the same training set, therefore the spreadsheet should be manually renamed after analysis is complete to prevent accidental deletion of results.
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|[11]||Generate Weights Use this button to generate the feature weights after properly configuring all of the above parameters. The generated weight file labels will appear in the MUD Files Pane and the classification results will appear in the Matlab command window and as figures.
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|[12]||Create R2 Plots Use this button to visualize the training data. This can be performed independently of the weight generation.
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'''''Feature Weights Pane'''''

MUD is BCI2000’s moniker for the feature weight matrix. As illustrated in Figure 1, *.mud files appear in the MUD Files Pane, numbered in order of simulation and having the following identifier: ‘''ChS1CAR0DF20RS100SW’'', where ChS1 indicates Channel Set 1, the value following CAR indicates whether a common average reference was used (0 no CAR used, 1 CAR used), the value following DF indicates the decimation factor, value following RS indicates the random sample value, and the final two characters indicate the regression method (SW: SWLDA, LS: least squares, or LG: logistic).
{|
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|[13]||Add *.mud Files Button Use this button to load previously saved *.mud files. The information for the selected files will appear below the button.
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|[14]||Clear All *.mud files Button Clears all feature weight (MUD) files currently stored in the GUI (those listed in the Feature Weights Pane). This does not delete previously saved *.mud or *.prm files.
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|[15]||File prefix Appends the specified prefix to the feature weight files when saved as a *.mud or *.prm for identification purposes.
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|[16]||File #’s to Save Used to specify the feature weight files to save according to those currently listed in the Feature Weights Pane. Multiple values can be entered that must be a subset of the currently listed of the feature weight files.
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|[17]||Save *.mud Button Generates *.mud files of the selected File #’s to Save in the training data directory. The file name contains the selected prefix and assigned identifier. The *.mud files are used solely for the purposes of the GUI to evaluate feature weights generated from prior sessions in the GUI.
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|[18]||Save *.prm Button Generates *.prm files of the selected File #’s to Save in the training data directory. The file name contains the selected prefix and assigned identifier. The *.prm files are BCI2000 parameter file fragments that can be imported into BCI2000 for online testing of the feature weights (see BCI2000 documentation for details). '''''Important: Only the BCI2000 parameters regarding the MUD weights, channels, and spatial filter are changed when the *.prm fragment is imported into BCI2000, be sure to verify the BCI2000 parameters before conducting an online session!!!'''''
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'''''Test Data Pane'''''
{|
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|[19]||Add Test files Button Use this button to load BCI2000 data files for testing of the feature weights. Multiple groupings of test files can be added for comparison.
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|[20]||Clear All Test files Button Clears all test files currently stored in the GUI (those listed in the Test Data Pane). This does not delete the actual data files.
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|[21]||Apply Weights Use this button to test the classification accuracy of each of the MUD files on each of the test files currently stored in the GUI. The classification results will appear in the Matlab command window and as figures.
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'''''Status Window'''''
{|
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|[22]||Messages and current status of the GUI are displayed in this window.
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===Training a Classifier===

The ' ''Training Data'' ' panel on the left of the GUI contains all of the parameters for generating SWLDA classifier weights from BCI2000 data files.

To generate SWLDA weights:

#Press the ’''Select Training Data''’ [1] button in the ’''Training Data''’ panel.
#From the dialog box, select the desired BCI2000 P300 *.dat file(s) for training.
#:;Notes:
#:*Selected files can be from different sessions of the same paradigm but must contain consistent parameters for proper execution and classification results. If the parameters are not consistent, Matlab may report an error during classification but the generated weights are likely still valid and can be tested because they are merely based on target/standard responses, not the characteristics of the stimuli.
#:*Files can only be selected from a single directory, the desired training files should be organized into the same directory prior to using this function.
#After all of the parameters are set as desired (see ''The GUI Control Panel'' section for details), press the ’''Generate Feature Weights''’ [11] button to perform the analysis. For each combination of parameters, the classification results will appear in the command window as they are generated. If the program was unable to generate a useable weight set for the data, this will also be indicated in the command window. When the analysis is completed, each set of MUD weights generated based on the different parameter combinations will appear in the ’''MUD files''’ pane and plots summarizing the results for each combination of set of MUD weights will appear (see Results Display section).
#The training procedure can be repeated multiple times with new MUD weights being appended to the current list in the ’''MUD Files''’ Pane.
#After training is completed and MUD weights are generated, it is recommended to test (cross validate) the MUD weights on independent data before saving. See the ''Testing a Classifier'' section for details on testing the MUD weights on independent data files. The weight files can now be saved (see ''Saving Classifier Weights'' section for details).

===Testing a Classifier===

The ’''Test Data''’ panel on the left of the GUI contains all of the controls for testing currently or previously created MUD weights to one or more BCI2000 test files

To apply weights to test files:

#In the ’''MUD files''’ panel, if no weights have been generated in the current session or additional weights are to be evaluated, press the ’''Add *.mud files''’ [13] button to add a single MUD file or generate new weights (see ‘''Training a Classifier''’ section for details). Repeat until all desired MUD files are loaded. All MUD files must have the same sampling rate and electrode montage. All currently loaded files can be cleared from the current session using the ’''Clear All *.mud Files''’ [14] button. Be sure to save any desired unsaved MUD files from a current session before clearing. All of the MUD files listed in the ’''Feature Weights''’ panel will be applied to the test data.
#In the ’''Test Data''’ panel, press the ’''Add Test Data''’ [19] button to add test files. One or more BCI2000 *.dat files can be selected from the same directory as a single 'test file group' each time the button is pressed. Repeat until all desired test file groups are loaded. All files selected should have the same sampling rate and electrode montage as the selected MUD files for valid classification. All currently loaded files can be cleared from the current session using the ’''Clear All Test Files''’ [20] button. All of the MUD files listed in the ’''MUD files''’ panel will be applied to each ’''test file group''’ listed in the 'Test Data' panel.
#After all of the MUD and test files are selected, press the ’''Apply Weights''’ [21] button to perform the analysis. For each combination of MUD and test file groups, the classification results will appear in the command window as they are generated. When the analysis is completed, plots summarizing the results for each test set will appear (see ''Results Display'' section).
#After evaluating the classification results, weight files from the current session can now be saved (see ''Saving Classifier Weights'' section for details).

===Results Display===

The results of all training and test simulations will be displayed in the Matlab command window and as individual performance plots as shown below. The command window (left) displays the predicted character at each intensification sequence for the corresponding feature weights as labeled. The performance curves for each set of feature weights are plotted in individual windows (right). The top and bottom figures in these windows indicate the same results with different plot styles. For test data, an additional plot will be displayed that gives the average of all the individual test data sets selected.

[[Image:GUI_Results.png]]

===Saving Classifier Weights===

The ’''Feature Weights''’ panel on the left of the GUI contains all of the controls for saving selected feature weights listed in the current pane for future use. The weights can be save as either *.mud or *.prm:

*.mud files are used solely for the purposes of the GUI to evaluate feature weights generated from prior sessions in the GUI.

*.prm files are BCI2000 parameter file fragments that can be imported into BCI2000 for online testing of the feature weights. '''''Important: Only the BCI2000 parameters regarding the MUD weights, channels, and spatial filter are changed when the *.prm fragment is imported into BCI2000, be sure to verify the BCI2000 parameters before conducting an online session!!! The specific parameters that are updated in the fragment are: MUD (feature weights), NumSamplesInERP(length of classified response in samples), SoftwareCh(# of total recording channels), SpatialFilteredChannels(channels that are included in the spatial filter), SpatialFilterKernel(weights for the spatial filter channels), and TransmitChList(channels that are used for classification).'''''

''Note: *prm files can be generated from *.mud files but *.mud file cannot be generated from *.prm files. It is recommended to save *.mud in addition to the *.prm for future analysis.''

To save one or more of the current feature weight files as a *.mud or *.prm file:

#Enter a prefix that will be appended to the MUD files (for identification purposes) in the ‘''File Prefix''’ [15] window.
#Enter the corresponding MUD file number(s) of the selected files in the ‘''File #'s to Save''’ [16] window and press Enter.
#Press the ‘''Save *.mud''‘ [17] or ‘''Save *.prm''‘ [18] button depending on the desired file type. The selected MUD files will be saved in the training data directory with a *.mud or a *.prm extension for later use. The files can be identified later by the name, for example, <tt>SessionA_ChS1CAR0DF20BPF0SW.mud</tt> and <tt>SessionA_ChS1CAR0DF20BPF0SW.prm</tt> In these names, 'SessionA_' is the chosen prefix, ChS1 indicates Channel Set 1, the value following CAR indicates whether a common average reference was used (0 no CAR used, 1 CAR used), the value following DF indicates the decimation factor, value following RS indicates the random sample value, and the final two characters indicate the regression method (SW: SWLDA, LS: least squares, or LG: logistic).
#See the BCI2000 documentation for instructions on how to load a *.prm fragment into a BCI2000 P3Speller or P3AV parameter file.

===Visualizing data===

Topographies, waveforms, and r2 plots can be generated independently of the other functions using the GUI as follows:

#Press the ‘''Select Training Data''‘ [1] button in the ‘''Training Data''‘ panel.
#From the dialog box, select the desired BCI2000 *.dat file(s) for visualizing.
#;Notes:
#*Default 16, 32, and 64 channel montages are provided. '''The electrode montage files (eloc*.txt - see EEGLab [1]) may need to be edited to match different montages.'''
#*Selected files can be from different sessions, but must use the same electrode montage and sampling rate to produce valid plots.
#*Files can only be selected from a single directory, the desired files should be organized into the same directory prior to using this function. 
#Select the desired window size [3] and spatial filter [5] from the drop-down menu in the ‘''Training Data''‘ panel. The ‘''Raw & CAR''‘ option will visualize the RAW data only! The other options will not effect the visualization.
#Press the ‘''Create R^2 Plots''‘ [12] button. When the processing is complete, an interactive figure will appear displaying the R^2 of the channels verses time (see bottom left). Left-click on the figure to display a plot of the waveform and topography at the specified channel/time location (see bottom right). Repeat to generate additional plots and right-click to finish and exit the interactive plotting mode.

[[Image:Visualizing_the_Data.png]]

==Troubleshooting==
Verify that you are using the latest release of the GUI (indicated at the bottom left-hand corner of the GUI control panel).

Errors are likely caused by inconsistent or corrupted data files. The GUI does not check all data file parameters for consistency or automatically adjust the default training parameters to the input data. Try to use data files from the same paradigm and configuration when possible.

For other difficulties, it is recommended to restart Matlab.

==References==
#Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics, Journal of Neuroscience Methods 134:9-21, 2004.
#Draper N, and Smith H. Applied Regression Analysis, 2nd edition, John Wiley and Sons, 1981, pp. 307-312.
#Farwell LA, Donchin E. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroenceph clin Neurophysiol 1988; 70: 510-23.
#Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR. Toward Enhanced P300 Speller Performance, Journal of Neuroscience Methods, 167:15-21, 2008.
#Krusienski DJ, Sellers EW, Cabestaing F, Bayoudh S, McFarland DJ, Vaughan TM, Wolpaw JR. A Comparison of Classification Techniques for the P300 Speller, Journal of Neural Engineering, 3:299-305, 2006.
#Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 2004; 51: 1034-43.
#Sellers EW, Donchin E. A P300-based brain-computer interface: Initial tests by ALS patients. Clin Neurophysiol: in press.
#Sharbrough F, Chatrian, CE, Lesser RP, Luders H, Nuwer M, and Picton TW. "American Electroencephalographic Society guidelines for standard electrode position nomenclature," J. Clin. Neurophysiol., vol. 8, pp. 200-202, 1991.

==Release Notes==
V2.0 09/30/05
:Original Release

V2.01 11/07/05
:Added 8 channel P300 montage

V2.02 12/15/05
:Now able to plot averages with unequal numbers of sequences for testing
:Added channel number checks

V2.03 12/21/05
:Changed to standard default analysis settings
:Made compatible with floating point (32) data

V2.04 12/23/05
:Added warning for unequal train and test sampling rates
:Corrected target definition to be printed character
:Added classification of spaces (_) and printing of backspaces (<)
:Updated to read latest BCI2000 header

V2.05 02/13/06
:Now compatible with new target definition matrix

V2.051 02/17/06
:Added fix for German characters - umlaut characters classified as standard characters

V2.052 02/21/06
:Made data loading more memory efficient

V2.06 02/22/06
:Added variable response window start point
:Implemented new function to load BCI2000 data
:Resized plot windows

V2.061 03/14/06
:Corrected classification for non-square matrices
:Changed to save MUD prior to training classification

V2.07 03/15/06
:Created a button to generate and save a BCI2000 parameter file fragment based on the selected MUD matrix

V2.08 03/30/06
:Added Least Squares and Logistic Regression options
:Fixed problem with *.prm Software Chs.
:Removed MA window from interface (now uses same values as DF)

V2.09 04/12/06
:Changed to compare row/column results rather than “Text to Spell’
:Encoded Online (“Free spelling”) data, assuming that the selected character is the target.

BCI2000 v2.0 Release

02/06/08
:Updated to be compatible and fully functional with BCI2000 v2.0 P3Speller data, and backward compatible with previous versions. This version has not been verified with P3AV data.
:Symbol rate figure replaces the percent correct ribbon figure.
:Fixed CAR filter generation.

==Contact/Credit==
Dean Krusienski 
Assistant Professor 
Electrical Engineering 
University of North Florida 
email: deankrusienski@ieee.org