AN ITERATIVE SPATIO-SPECTRAL DISCRIMINANT SCHEME FOR EEG CLASSIFICATION

Abstract

Abstract– Brain Computer Interface (BCI) systems still suffer from lack of accuracy in real-timeapplications. This problem emerges from isolated optimization, and in some occasions frommismatching of feature extraction and classification stages. To unify optimization of both stages,this paper presents a novel scheme to integrate them and simultaneously optimize under a unitcriterion. The proposed method iteratively estimates both spatio-spectral filters and classifierweights under a non-linear form of Fisher criterion. In order to validate the introduced method,two standard EEG sets, one containing 118 EEG signals and the other 29, were employed todemonstrate its spatial resolution capability. Experimental results on both datasets reveal thesuperiority of the proposed scheme in terms of enhancing the classification performancesimultaneously with speeding up the optimization process, compared to the conventional methods.