User Tutorial:Data Analysis: Difference between revisions
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There is a number of data analysis tutorials provided. These show how to analyze some sample data that is included with ''BCI2000''. | There is a number of data analysis tutorials provided. These show how to analyze some sample data that is included with ''BCI2000''. | ||
==Mu Rhythm/SMR Data Analysis== | ==Mu Rhythm/SMR Data Analysis== | ||
| Line 34: | Line 29: | ||
==Custom Data Analysis== | ==Custom Data Analysis== | ||
For users who wish to go beyond the constraints of the analysis procedures in the tutorials, the following resources may be useful: | BCI2000 data files can be loaded, read, and analyzed in both Matlab and Python. For users who wish to go beyond the constraints of the analysis procedures in the tutorials, the following resources may be useful: | ||
* The [[User_Reference:Command_Line_Processing|BCI2000 command-line tools]] can be used to recreate a chain of BCI2000 processing steps offline, from a system command-line. | * The [[User_Reference:Command_Line_Processing|BCI2000 command-line tools]] can be used to recreate a chain of BCI2000 processing steps offline, from a system command-line. | ||
* The Matlab function <tt>bci2000chain</tt>, part of BCI2000's suite of [[User_Reference:Matlab_Tools|Matlab tools]], provides a convenient interface to this from within Matlab. | * The Matlab function <tt>bci2000chain</tt>, part of BCI2000's suite of [[User_Reference:Matlab_Tools|Matlab tools]], provides a convenient interface to this from within Matlab. | ||
** Converting and Analyzing BCI2000 Data with [[User Reference:Matlab MEX Files|Matlab MEX Files]] | |||
* Analyze BCI2000 Data in Python with [https://github.com/neurotechcenter/BCI2kReader BCI2k Reader] | |||
==See also== | ==See also== | ||
Revision as of 16:01, 20 April 2022
There is a number of data analysis tutorials provided. These show how to analyze some sample data that is included with BCI2000.
Mu Rhythm/SMR Data Analysis
- will guide you through an offline analysis using some sample EEG data included with BCI2000.
- will compare the results of the previous step with other similar data.
As we know, EEG data is not the only type of data that is of interest in BCI. Following are some additional tutorials for working with different types of datasets:
- will guide you through an offline analysis using some sample ECoG data included with BCI2000
- will guide you through an offline analysis using some sample MEG data included with BCI2000
P300/ERP Data Analysis
- will guide you through a time-domain offline analysis using some sample EEG data included with BCI2000.
As we know, EEG data is not the only type of data that is of interest in BCI. Following is an additional tutorial for working with ECoG datasets:
- will guide you through a time-domain offline analysis using some sample ECoG data included with BCI2000
- will show you how to train a classifier, and to obtain a set of configuration parameters, from data recorded with StimulusPresentation or P3Speller application modules.
Custom Data Analysis
BCI2000 data files can be loaded, read, and analyzed in both Matlab and Python. For users who wish to go beyond the constraints of the analysis procedures in the tutorials, the following resources may be useful:
- The BCI2000 command-line tools can be used to recreate a chain of BCI2000 processing steps offline, from a system command-line.
- The Matlab function bci2000chain, part of BCI2000's suite of Matlab tools, provides a convenient interface to this from within Matlab.
- Converting and Analyzing BCI2000 Data with Matlab MEX Files
- Analyze BCI2000 Data in Python with BCI2k Reader
See also
User Tutorial:BCI2000 Tour, User Tutorial:Mu Rhythm BCI Tutorial, User Tutorial:P300 BCI Tutorial