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 th...

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Bibliographic Details
Main Authors: Kyeong-Yeon Lee, Sun Kim
Format: Conference Proceeding
Language:English
Subjects:
Brain-Computer Interface (BCI)
Common Spatial Pattern (CSP)
Educational institutions
Eigenvalues and eigenfunctions
Electroencephalography
Entropy
Feature extraction
Feature Selection
Motor Imagery Classification
Mutual information
Vectors
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Summary: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.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2012.6377856