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Combined Approaches of Features Selection for EEG Classification

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The classification accuracy of EEG signal illustrates the degree of recognition of significant characteristics describing events revealing underlying neuronal activity. In this paper we focus on features selection, by using both t-test and entropy criteria. The feature extraction is accomplished using wavelet functions Symmlet5, Coiflet5, and DB10, and classification is achieved using Linear Discriminate Analysis and Support Vector Machines classifiers. The obtained results show an improvement in classification accuracy reaching 7% for LDA classifier, and 4% for SVM classifier.
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Classification; EEG; Entropy; Features selection; T-Test

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