AN ITERATIVE SPATIO-SPECTRAL DISCRIMINANT SCHEME FOR EEG CLASSIFICATION

Abstract– Brain Computer Interface (BCI) systems still suffer from lack of accuracy in real-time
applications. This problem emerges from isolated optimization, and in some occasions from
mismatching 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 unit
criterion. The proposed method iteratively estimates both spatio-spectral filters and classifier
weights 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 to
demonstrate its spatial resolution capability. Experimental results on both datasets reveal the
superiority of the proposed scheme in terms of enhancing the classification performance
simultaneously with speeding up the optimization process, compared to the conventional methods.