VisualizeBCI2000 - Revision history

Wengelhardt at 16:39, 24 June 2025

2025-06-24T16:39:06Z

← Older revision		Revision as of 16:39, 24 June 2025
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	~~[[Image:CCEP gif.gif \|900px\|right\|thumb\|frame\| An example video showing the CCEP filter with VisualizeBCI2000.]]~~

			[[Image:CCEP gif.gif\|thumb\|960px\|An example video showing the CCEP filter with VisualizeBCI2000.]]
			<div style="clear: both"></div>
	=Introduction=		=Introduction=
	A Python toolkit to powerfully visualize BCI2000 data in real-time. The incoming data can be pre-processed in BCI2000's pipeline, or not, then sent to python through BCI2000's [[User Reference:SignalSharing\|SignalSharing]] feature. The data can also be visualized in 3D if combined with a [https://github.com/neurotechcenter/VERA VERA structure (electrode localization tool)]. It is designed based on BCI2000's standard of modularity, therefore any of these components can be swapped out without changing the rest of the pipeline.		A Python toolkit to powerfully visualize BCI2000 data in real-time. The incoming data can be pre-processed in BCI2000's pipeline, or not, then sent to python through BCI2000's [[User Reference:SignalSharing\|SignalSharing]] feature. The data can also be visualized in 3D if combined with a [https://github.com/neurotechcenter/VERA VERA structure (electrode localization tool)]. It is designed based on BCI2000's standard of modularity, therefore any of these components can be swapped out without changing the rest of the pipeline.

	=Installation=		=Installation=
	'''[https://github.com/neurotechcenter/VisualizeBCI2000 VisualizeBCI2000 can be downloaded from GitHub here]'''		'''[https://github.com/neurotechcenter/VisualizeBCI2000 VisualizeBCI2000 can be downloaded from GitHub here]'''

Wengelhardt: /* Installation */

2025-04-29T18:45:05Z

Installation

← Older revision		Revision as of 18:45, 29 April 2025
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	#*PyQtGraph 0.13.3+: <code>pip install pyqtgraph</code>		#*PyQtGraph 0.13.3+: <code>pip install pyqtgraph</code>
	#*SciPy: <code>pip install scipy</code> (only the stats package is required)		#*SciPy: <code>pip install scipy</code> (only the stats package is required)
			#*PyOpenGL: <code>pip install PyOpenGL PyOpenGL_accelerate</code>

	=Set-up=		=Set-up=

Wengelhardt: /* Settings */

2025-04-29T17:23:00Z

Settings

← Older revision		Revision as of 17:23, 29 April 2025
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	* '''Filters''': The BCI2000 Signal Processing filter which sends the incoming data. Each filter requires a custom Python implementation. Currently, the only 2 filters that are available are:		* '''Filters''': The BCI2000 Signal Processing filter which sends the incoming data. Each filter requires a custom Python implementation. Currently, the only 2 filters that are available are:
	** '''''[[Contributions:CCEPFilter\|CCEPFilter]]'''''		** '''''[[Contributions:CCEPFilter\|CCEPFilter]]'''''
			** '''''[[Contributions:PAC\|Phase-amplitude coupling (PACFilter)]]'''''
	** '''''DataIOFilter''''': This streams the raw data from BCI2000 and continually updates a grid of plots. This is less of a useful visualization, and more of an example for a basic visualization.		** '''''DataIOFilter''''': This streams the raw data from BCI2000 and continually updates a grid of plots. This is less of a useful visualization, and more of an example for a basic visualization.
	* '''Data Streams''': Defaults to BCI2000. If other data streams are desired, these must be implemented.		* '''Data Streams''': Defaults to BCI2000. If other data streams are desired, these must be implemented.

Wengelhardt at 17:19, 29 April 2025

2025-04-29T17:19:46Z

@@ Line 7: / Line 7: @@
 '''[https://github.com/neurotechcenter/VisualizeBCI2000 VisualizeBCI2000 can be downloaded from GitHub here]'''
-#Python 3.7+ needs to be installed. [https://www.python.org/downloads/| You can download the lastest version here].
+#Python 3.8+ needs to be installed. [https://www.python.org/downloads/| You can download the lastest version here].
 #A couple additional packages are used in this visualization. Run the following segments using <code>pip</code> in the command line.
 #*Numpy: <code>pip install numpy</code>
@@ Line 13: / Line 13: @@
 #*PyQtGraph 0.13.3+: <code>pip install pyqtgraph</code>
 #*SciPy: <code>pip install scipy</code> (only the stats package is required)
 =Settings=

Wengelhardt at 20:45, 24 April 2025

2025-04-24T20:45:47Z

← Older revision		Revision as of 20:45, 24 April 2025
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	=Installation=		=Installation=
			'''[https://github.com/neurotechcenter/VisualizeBCI2000 VisualizeBCI2000 can be downloaded from GitHub here]'''

	#Python 3.7+ needs to be installed. [https://www.python.org/downloads/\| You can download the lastest version here].		#Python 3.7+ needs to be installed. [https://www.python.org/downloads/\| You can download the lastest version here].
	#A couple additional packages are used in this visualization. Run the following segments using <code>pip</code> in the command line.		#A couple additional packages are used in this visualization. Run the following segments using <code>pip</code> in the command line.
Line 19:		Line 21:
	** '''''DataIOFilter''''': This streams the raw data from BCI2000 and continually updates a grid of plots. This is less of a useful visualization, and more of an example for a basic visualization.		** '''''DataIOFilter''''': This streams the raw data from BCI2000 and continually updates a grid of plots. This is less of a useful visualization, and more of an example for a basic visualization.
	* '''Data Streams''': Defaults to BCI2000. If other data streams are desired, these must be implemented.		* '''Data Streams''': Defaults to BCI2000. If other data streams are desired, these must be implemented.

			=3D Visualization=
			To enable the 3D visualization, you must input a VERA structure. VERA is an electrode localization application made to simplify electrode localization. The output of the VERA pipeline is a .mat (Matlab) structure, which is the input to be loaded into VisualizeBCI2000. In order to match the electrode names with the BCI2000 channel names, there must be a field called ''electrodeNamesKey'', with columns ''VERANames'' and ''EEGNames''. This table provides a necessary key between the two names.

Wengelhardt at 20:34, 24 April 2025

2025-04-24T20:34:44Z

← Older revision		Revision as of 20:34, 24 April 2025
Line 1:		Line 1:
			[[Image:CCEP gif.gif \|900px\|right\|thumb\|frame\| An example video showing the CCEP filter with VisualizeBCI2000.]]

			=Introduction=
	A Python toolkit to powerfully visualize BCI2000 data in real-time. The incoming data can be pre-processed in BCI2000's pipeline, or not, then sent to python through BCI2000's [[User Reference:SignalSharing\|SignalSharing]] feature. The data can also be visualized in 3D if combined with a [https://github.com/neurotechcenter/VERA VERA structure (electrode localization tool)]. It is designed based on BCI2000's standard of modularity, therefore any of these components can be swapped out without changing the rest of the pipeline.		A Python toolkit to powerfully visualize BCI2000 data in real-time. The incoming data can be pre-processed in BCI2000's pipeline, or not, then sent to python through BCI2000's [[User Reference:SignalSharing\|SignalSharing]] feature. The data can also be visualized in 3D if combined with a [https://github.com/neurotechcenter/VERA VERA structure (electrode localization tool)]. It is designed based on BCI2000's standard of modularity, therefore any of these components can be swapped out without changing the rest of the pipeline.

			=Installation=
			#Python 3.7+ needs to be installed. [https://www.python.org/downloads/\| You can download the lastest version here].
			#A couple additional packages are used in this visualization. Run the following segments using <code>pip</code> in the command line.
			#*Numpy: <code>pip install numpy</code>
			#*PyQt5: <code>pip install PyQt5</code>
			#*PyQtGraph 0.13.3+: <code>pip install pyqtgraph</code>
			#*SciPy: <code>pip install scipy</code> (only the stats package is required)

			=Settings=
			These Settings are available to configure in the toolbar
			* '''Choose BCI2000 Location''': Choose the directory in which your BCI2000 resides. The folder chosen should be the main, root folder. This is mainly used to access the BCI2000Remote files, which are assumed to be located in the ''prog'' folder of the root directory.
			* '''Filters''': The BCI2000 Signal Processing filter which sends the incoming data. Each filter requires a custom Python implementation. Currently, the only 2 filters that are available are:
			** '''''[[Contributions:CCEPFilter\|CCEPFilter]]'''''
			** '''''DataIOFilter''''': This streams the raw data from BCI2000 and continually updates a grid of plots. This is less of a useful visualization, and more of an example for a basic visualization.
			* '''Data Streams''': Defaults to BCI2000. If other data streams are desired, these must be implemented.

Wengelhardt: Created page with "A Python toolkit to powerfully visualize BCI2000 data in real-time. The incoming data can be pre-processed in BCI2000's pipeline, or not, then sent to python through BCI2000's SignalSharing feature. The data can also be visualized in 3D if combined with a [https://github.com/neurotechcenter/VERA VERA structure (electrode localization tool)]. It is designed based on BCI2000's standard of modularity, therefore any of these components can be..."

2025-04-23T21:19:20Z

Created page with "A Python toolkit to powerfully visualize BCI2000 data in real-time. The incoming data can be pre-processed in BCI2000's pipeline, or not, then sent to python through BCI2000's SignalSharing feature. The data can also be visualized in 3D if combined with a [https://github.com/neurotechcenter/VERA VERA structure (electrode localization tool)]. It is designed based on BCI2000's standard of modularity, therefore any of these components can be..."

New page

A Python toolkit to powerfully visualize BCI2000 data in real-time. The incoming data can be pre-processed in BCI2000's pipeline, or not, then sent to python through BCI2000's [[User Reference:SignalSharing|SignalSharing]] feature. The data can also be visualized in 3D if combined with a [https://github.com/neurotechcenter/VERA VERA structure (electrode localization tool)]. It is designed based on BCI2000's standard of modularity, therefore any of these components can be swapped out without changing the rest of the pipeline.