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Optimizing Spectral Filters for Single Trial EEG Classification

  • Ryota Tomioka
  • Guido Dornhege
  • Guido Nolte
  • Kazuyuki Aihara
  • Klaus-Robert Müller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)

Abstract

We propose a novel spectral filter optimization algorithm for the single trial ElectroEncephaloGraphy (EEG) classification problem. The algorithm is designed to improve the classification accuracy of Common Spatial Pattern (CSP) based classifiers. The algorithm is based on a simple statistical criterion, and allows the user to incorporate any prior information one has about the spectrum of the signal. We show that with a different preprocessing, how a prior knowledge can drastically improve the classification or only be misleading. We also show a generalization of the CSP algorithm so that the CSP spatial projection can be recalculated after the optimization of the spectral filter. This leads to an iterative procedure of spectral and spatial filter update that further improves the classification accuracy, not only by imposing a spectral filter but also by choosing a better spatial projection.

Keywords

Linear Discriminant Analysis Spectral Filter Common Spatial Pattern Spatial Projection Motor Imagery Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ryota Tomioka
    • 1
    • 2
  • Guido Dornhege
    • 2
  • Guido Nolte
    • 2
  • Kazuyuki Aihara
    • 1
  • Klaus-Robert Müller
    • 2
  1. 1.Dept. Mathematical Informatics, ISTThe University of TokyoTokyoJapan
  2. 2.Fraunhofer FIRST.IDABerlinGermany

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