Discriminative spatial pattern vectors selection for motor imagery classification

In this paper, we propose a novel method of designing a class-discriminative spatial filter assuming that a combination of spatial pattern vectors, irrespective of the eigenvalues, can produce better performance in terms of classification accuracy. We select discriminative spatial pattern vectors that determine features in a pairwise manner, i.e., eigenvectors of the k-th largest eigenvalue and the k-the lowest 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 experiments, the proposed method outperformed the conventional approach.