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Cognitive Neurodynamics
Published in: Cognitive Neurodynamics 4/2010

01-12-2010 | Research article

Generalized optimal spatial filtering using a kernel approach with application to EEG classification

Authors: Qibin Zhao, Tomasz M. Rutkowski, Liqing Zhang, Andrzej Cichocki

Published in: Cognitive Neurodynamics | Issue 4/2010

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Abstract

Common spatial patterns (CSP) has been widely used for finding the linear spatial filters which are able to extract the discriminative brain activities between two different mental tasks. However, the CSP is difficult to capture the nonlinearly clustered structure from the non-stationary EEG signals. To relax the presumption of strictly linear patterns in the CSP, in this paper, a generalized CSP (GCSP) based on generalized singular value decomposition (GSVD) and kernel method is proposed. Our method is able to find the nonlinear spatial filters which are formulated in the feature space defined by a nonlinear mapping through kernel functions. Furthermore, in order to overcome the overfitting problem, the regularized GCSP is developed by adding the regularized parameters. The experimental results demonstrate that our method is an effective nonlinear spatial filtering method.
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Metadata
Title
Generalized optimal spatial filtering using a kernel approach with application to EEG classification
Authors
Qibin Zhao
Tomasz M. Rutkowski
Liqing Zhang
Andrzej Cichocki
Publication date
01-12-2010
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics / Issue 4/2010
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-010-9125-x

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