Classifying n-back EEG data using entropy and mutual information features

In this work we show that entropy (H) and mutual information (MI) can be used as methods for extracting spatially localized features for classification purposes. In order to increase accuracy of entropy estimation, we use a Bayesian approach with a Dirichlet prior to derive estimation equations. We calculate the H and MI features for each electrode (H) and pair of electrodes (MI) in three frequency bands and use them to train the Naive Bayes classifier. We test the H and MI features on one/five trial long segments of n-back memory EEG signals and show that they outperform power spectrum and linear correlation features respectively.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Liang Wu, Predrag Neskovic, Etienne Reyes, Elena Festa, Heindel William
• Keywords: Entropy Estimation, ESANN 2007, MI Features, Naive Bayes Classifier, Neural Networks