Classifying EEG data into different memory loads across subjects

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. © Springer-Verlag Berlin Heidelberg 2007.