Wrapper functions for electrophysiological data classification in MATLAB.
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README.md

README.md

BSClassify

BrainStateClassify (BSClassify) are wrapper functions written for MATLAB (The MathWorks, Inc., Natick, Massachusetts, United States) designed to classify brain state data, e.g. from electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings, using a linear support vector machine classifier in the implementation by Chang & Lin (2011). Three performance measures of classification are obtained in a 10-fold cross-validation.

Feature Reduction

Input data is subjected to a feature reduction step using R^2-values after Pearson's correlation coefficient (Spüler et. al. 2011). By default, only features exceeding the mean of R^2-values are considered for classification. Depending on the classification problem and data at hand, the number of features should be changed (see line 60 in 'mlSVMR2.m').

Usage

Import the source folder to your MATLAB path. Make sure correct libsvm binaries are also added to your path. Then simply execute the following. Exchange datxDummy and datyDummy with your real data and labels.

% example.m
datxDummy = rand(100,29,100); 	% generate random dummy data
datyDummy = ones(100,1);		% generate dummy labels 
datyDummy(51:100) = 2;			% balanced classes 50/50
[acc, auc, cf1] = mlSVMR2( datxDummy, datyDummy );	% classify and compute performance

About

I'm with the institute of Medical Psychology and Behavioral Neurobiology and the Computer Science department of the University of Tuebingen, Germany. I'm fascinated by machine learning. If you want to say hello or have a question, please drop me an e-mail.

dthettich@gmail.com

Literature

  • Spueler, M., Rosenstiel, W., & Bogdan, M. (2011). A fast feature selection method for high-dimensional meg bci data. In Proceedings of the 5th Int. Brain-Computer Interface Conference, (pp. 24–27).

  • Chang, C.-C. & Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.