Feature selection study of P300 speller using support vector machine

Abstract

P300 speller is a traditional brain computer interface paradigm and focused by lots of current BCI researches. In this paper a support vector machine based recursive feature elimination method was adapted to select the optimal channels for character recognition. The margin distance between target and nontarget stimulus in feature space was evaluated by training SVM classifier and then the features from single channel were eliminated one by one, eventually, channel set provided best recognition performance was left as the optimal set. The results showed that using optimal channel set would achieve a higher recognition correct ratio compared with no channel eliminating. Furthermore the optimal features localized on parietal and occipital areas, on which not only P300 components but VEP components also present a high amplitude waveform. It may suggest that row/column intensification in speller matrix arouses a visual evoked potential and contributes a lot to character identification as well as P300.