The problem of a volume conduction effect in electroencephalography is considered one of the challenging issues in brain-computer interface (BCI) community. In this article, we propose a novel method of designing a class-discriminative spatial filter assuming that a combination of spatial pattern vectors, irrespective of the eigenvalues of the common spatial pattern (CSP), can produce better performance in terms of classification accuracy. We select discriminative spatial filter vectors that determine features in a pairwise manner, that is, eigenvectors of the K largest eigenvalue and the K smallest eigenvalue. Although the pair of the eigenvectors of the K largest and the K smallest eigenvalues helps extract discriminative features, we believe that a different set of eigenvector pairs is more appropriate to extract class-discriminative features. In our experimental results using the publicly available dataset of BCI Competition IV, we show that the proposed method outperformed the conventional CSP methods and a filter-bank CSP.

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