A treatment of EEG data by underdetermined blind source separation for motor imagery classification

Zbynek Koldovsky, Anh Huy Phan, Petr Tichavsky, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Brain-Computer Interfaces (BCI) controlled through imagined movements cannot work properly without a correct classification of EEG signals. The difficulty of this problem consists in low signal-to-noise ratio, because EEG may contain strong signal components that are not related to motor imagery. In this paper, these artifact components are to be suppressed using a recently proposed underdetermined blind source separation method and a novel MMSE beamformer. We use these tools to remove unwanted components of EEG to increase the classification accuracy of the BCI system. In our experiments with several datasets, the classification is improved by up to 10%.

Original languageEnglish
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Pages1484-1488
Number of pages5
Publication statusPublished - 2012
Externally publishedYes
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: 27 Aug 201231 Aug 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference20th European Signal Processing Conference, EUSIPCO 2012
Country/TerritoryRomania
CityBucharest
Period27/08/1231/08/12

Keywords

  • Beamforming
  • Brain-Computer Interface
  • Electroencephalogram
  • Underdetermined Blind Source Separation

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