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

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

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)355-358
Number of pages4
JournalCognitive Neurodynamics
Issue number4
Publication statusPublished - Dec 2010
Externally publishedYes


  • BCI
  • CSP
  • EEG
  • Kernel method


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