Extraction and classification of visual evoked potentials based on a two-stage source extraction algorithm

In order to verify whether or not the EEG patterns can be classified when the subjects perceive different types of geometric figures, we perform some EEG experiments. In this paper the evoked potentials by three types of geometric figures are extracted and classified using a series of approaches. First, a two-stage source extraction algorithm is proposed to extract the evoked potentials from the recorded EEG signals, and then a mutual information based feature selection method is presented to find effective features for classification. Finally, a multi-category support vector machine classifier is employed, which achieves the average classification performance of 93.2%.