A maxmin approach to optimize spatial filters for eeg single-trial classification

Motoaki Kawanabe, Carmen Vidaurre, Benjamin Blankertz, Klaus Muller

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

7 Citations (Scopus)

Abstract

Electroencephalographic single-trial analysis requires methods that are robust with respect to noise, artifacts and non-stationarity among other problems. This work contributes by developing a maxmin approach to robustify the common spatial patterns (CSP) algorithm. By optimizing the worst-case objective function within a prefixed set of the covariance matrices, we can transform the respective complex mathematical program into a simple generalized eigenvalue problem and thus obtain robust spatial filters very efficiently. We test our maxmin CSP method with real world brain-computer interface (BCI) data sets in which we expect substantial fluctuations caused by day-to-day or paradigm-to-paradigm variability or different forms of stimuli. The results clearly show that the proposed method significantly improves the classical CSP approach in multiple BCI scenarios.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages674-682
Number of pages9
Volume5517 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2009 Aug 20
Externally publishedYes
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain
Duration: 2009 Jun 102009 Jun 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5517 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th International Work-Conference on Artificial Neural Networks, IWANN 2009
CountrySpain
CitySalamanca
Period09/6/1009/6/12

    Fingerprint

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kawanabe, M., Vidaurre, C., Blankertz, B., & Muller, K. (2009). A maxmin approach to optimize spatial filters for eeg single-trial classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 5517 LNCS, pp. 674-682). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5517 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-02478-8_84