Spatial filter design based on re-estimated projection matrices

In this paper, motor imagery electroencephalograph classification problem is investigated and a method which modifies the projection matrix is proposed based on common spatial pattern analysis. Exceptional samples are detected through examining the features generated by the projection matrix in the first place, which are special in terms that the projection matrix in common spatial pattern analysis fails to extract discriminant features from them. Projection matrices for exceptional trials are re-estimated and integrated together to form the final projection model. Based on this integrated model, feature extraction is carried out and classification follows by employing support vector machine. The validity of the proposed method is verified through experiment studies. Two data sets that consist of two classes are used, and results show that the proposed method generates more discriminant features.