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 160Hz. The classification rates are statistically significant and the best classification rates, close to 90%, are obtained using conditional entropy features.

• Type: Conference
• Year: 2007
• Where: ICANN
• Authors: Liang Wu, Predrag Neskovic
• Keywords: Conditional Entropy, Conditional Entropy Features, Conditional Mutual Information, ICANN 2007, Neural Networks