Classifying EEG data into different memory loads across subjects

Authors:
Liang Wu;Predrag Neskovic
Affiliations:
Department of Physics and Institute for Brain and Neural Systems, Brown University, Providence, RI;Department of Physics and Institute for Brain and Neural Systems, Brown University, Providence, RI
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

In this paper we consider the question of whether it is possible to classify n-back EEG data into different memory loads across subjects. To capture relevant information from the EEG signal we use three types of features: power spectrum, conditional entropy, and conditional mutual information. In order to reduce irrelevant and misleading features we use a feature selection method that maximizes mutual information between features and classes and minimizes redundancy among features. Using a selected group of features we show that all classifiers can successfully generalize to the new subject for bands 1-40Hz and 1-60Hz. The classification rates are statistically significant and the best classification rates, close to 90%, are obtained using conditional entropy features.