User Tutorial:Analyzing the Initial Mu Rhythm Session - Revision history

Ckong: /* Generating a Feature Plot */

2019-07-25T15:11:48Z

Generating a Feature Plot

← Older revision		Revision as of 15:11, 25 July 2019
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	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 movements 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.		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 movements 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.

	[[Image:OfflineANA.PNG\|right\|~~500px~~]]		[[Image:OfflineANA.PNG\|right\|700px]]

	To generate a feature plot from the initial session's data, perform the following steps:		To generate a feature plot from the initial session's data, perform the following steps:

Ckong: /* Generating a Feature Plot */

2019-07-25T14:34:10Z

Generating a Feature Plot

← Older revision		Revision as of 14:34, 25 July 2019
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	*Click "Generate Plots", and wait for the feature plot to appear.		*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~~).		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 movements).

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

Ckong: /* Generating a Feature Plot */

2019-07-25T14:15:57Z

Generating a Feature Plot

← Older revision		Revision as of 14:15, 25 July 2019
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	==Generating a Feature Plot==		==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.		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 movements 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.

			[[Image:OfflineANA.PNG\|right\|500px]]

	To generate a feature plot from the initial session's data, perform the following steps:		To generate a feature plot from the initial session's data, perform the following steps:

Mellinger: /* Generating Spectra and Topography Plots */

2012-07-05T12:52:18Z

Generating Spectra and Topography Plots

← Older revision		Revision as of 12:52, 5 July 2012
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	==Generating Spectra and Topography Plots==		==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.		*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 analysis program's '''Spectra Channels''' field, enter the channels you read off the feature map.
	*In the '''Topo Frequencies''' field, enter the frequencies 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.		*Click the '''Generate Plots''' button.

Mellinger: /* Generating a Feature Plot */

2012-07-05T12:51:40Z

Generating a Feature Plot

← Older revision		Revision as of 12:51, 5 July 2012
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	*In the '''Acquisition Type''' field, choose "EEG".		*In the '''Acquisition Type''' field, choose "EEG".
	*As a '''Spatial Filter''', choose "Common Average Reference (CAR)".		*As a '''Spatial Filter''', choose "Common Average Reference (CAR)".
	*Enter <tt>~~states.StimulusBegin == 1~~</tt> into the '''Trial Change Condition''' field.		*Enter <tt>auto</tt> into the '''Trial Change Condition''' field.
	*Into the field labeled '''Target Condition 1''', enter <tt>states.StimulusCode == 0</tt>.		*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'''.		*Enter the word "Rest" into the field labeled '''Target Condition Label 1'''.

Mellinger: /* Picking Optimal Features */

2009-08-25T16:09:07Z

Picking Optimal Features

← Older revision		Revision as of 16:09, 25 August 2009
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	==Picking Optimal Features==		==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.		Here we are looking for the frequency with the highest r-squared value 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.		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.

Awilson at 14:35, 25 August 2009

2009-08-25T14:35:41Z

← Older revision		Revision as of 14:35, 25 August 2009
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	*As a '''Spatial Filter''', choose "Common Average Reference (CAR)".		*As a '''Spatial Filter''', choose "Common Average Reference (CAR)".
	*Enter <tt>states.StimulusBegin == 1</tt> into the '''Trial Change Condition''' field.		*Enter <tt>states.StimulusBegin == 1</tt> into the '''Trial Change Condition''' field.
	*Into the field labeled '''Target Condition 1''', enter <tt>states.StimulusCode == 1</tt>.		*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'''.		*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'''.		*Similarly, enter <tt>states.StimulusCode == 2</tt> into the '''Target Condition 2''' field, and "Right Hand" into '''Target Condition Label 2'''.

Awilson at 14:30, 25 August 2009

2009-08-25T14:30:52Z

← Older revision		Revision as of 14:30, 25 August 2009
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	*As a '''Spatial Filter''', choose "Common Average Reference (CAR)".		*As a '''Spatial Filter''', choose "Common Average Reference (CAR)".
	*Enter <tt>states.StimulusBegin == 1</tt> into the '''Trial Change Condition''' field.		*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>.		*Into the field labeled '''Target Condition 1''', enter <tt>states.StimulusCode == 1</tt>.
	*Enter the word "Rest" into the field labeled '''Target Condition Label 1'''.		*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'''.		*Similarly, enter <tt>states.StimulusCode == 2</tt> into the '''Target Condition 2''' field, and "Right Hand" into '''Target Condition Label 2'''.

Mellinger: /* Generating a Feature Plot */

2009-08-18T14:28:16Z

Generating a Feature Plot

← Older revision		Revision as of 14:28, 18 August 2009
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	*Enter the word "Rest" into the field labeled '''Target Condition Label 1'''.		*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'''.		*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 the "Add" button located besides the '''Data Files''' field. A file chooser dialog will appear; navigate to <tt>data/mu/<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.		*Click "Generate Plots", and wait for the feature plot to appear.

Mellinger: /* Generating a Feature Plot */

2009-06-02T13:10:56Z

Generating a Feature Plot

← Older revision		Revision as of 13:10, 2 June 2009
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	*Start the [[User Reference:BCI2000 Offline Analysis\|BCI2000 Offline Analysis]] tool:		*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>.		**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~~]].		**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\|elsewhere]].
	*In the '''Analysis Domain''' field, choose ''Frequency''.		*In the '''Analysis Domain''' field, choose ''Frequency''.
	*In the '''Acquisition Type''' field, choose "EEG".		*In the '''Acquisition Type''' field, choose "EEG".