Optimizing spectral filters for single trial EEG classification

Ryota Tomioka, Guido Dornhege, Guido Nolte, Kazuyuki Aihara, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationPattern Recognition - 28th DAGM Symposium, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540444122, 9783540444121
Publication statusPublished - 2006
Externally publishedYes
Event28th Symposium of the German Association for Pattern Recognition, DAGM 2006 - Berlin, Germany
Duration: 2006 Sept 122006 Sept 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4174 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other28th Symposium of the German Association for Pattern Recognition, DAGM 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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