Cognitive Neurodynamics

, Volume 4, Issue 4, pp 355–358 | Cite as

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

  • Qibin Zhao
  • Tomasz M. Rutkowski
  • Liqing Zhang
  • Andrzej Cichocki
Research article

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.

Keywords

EEG BCI Kernel method CSP 

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Qibin Zhao
    • 1
  • Tomasz M. Rutkowski
    • 1
  • Liqing Zhang
    • 2
  • Andrzej Cichocki
    • 1
  1. 1.Laboratory for Advanced Brain Signal Processing, Brain Science InstituteRIKENSaitamaJapan
  2. 2.MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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