Abstract
In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method.
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Acknowledgments
The author would like to express his gratitude to the anonymous referees for valuable suggestions. This work was supported in part by the National Natural Science Foundation of China under grants 61075009, 60803059 and 10871001, the Qing Lan Project, and the Fund for the Program of Excellent Young Teachers at Southeast University.
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Wang, H. Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion. Med Biol Eng Comput 49, 997–1001 (2011). https://doi.org/10.1007/s11517-011-0766-7
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DOI: https://doi.org/10.1007/s11517-011-0766-7