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  EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods

Schröder, M., Lal, T., Bogdan, M., & Schölkopf, B. (2004). EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods. Poster presented at 7th Tübingen Perception Conference (TWK 2004), Tübingen, Germany.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D9E5-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-65A1-3
Genre: Poster

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 Creators:
Schröder, M, Author              
Lal, TN1, 2, Author              
Bogdan, M, Author
Schölkopf, B1, 2, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: A Brain Computer Interface (BCI) system allows the direct interpretation of brain activity patterns (e.g. EEG signals) by a computer. Typical BCI applications comprise spelling aids or environmental control systems supporting paralyzed patients that have lost motor control completely. The design of an EEG based BCI system requires good answers for the problem of selecting useful features during the performance of a mental task as well as for the problem of classifying these features. For the special case of choosing appropriate EEG channels from several available channels, we propose the application of variants of the Support Vector Machine (SVM) for both problems. Although these algorithms do not rely on prior knowledge they can provide more accurate solutions than standard lter methods [1] for feature selection which usually incorporate prior knowledge about neural activity patterns during the performed mental tasks. For judging the importance of features we introduce a new relevance measure and apply it to EEG channels. Although we base the relevance measure for this purpose on the previously introduced algorithms, it does in general not depend on specic algorithms but can be derived using arbitrary combinations of feature selectors and classifiers.

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Language(s):
 Dates: 2004-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 2539
 Degree: -

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Title: 7th Tübingen Perception Conference (TWK 2004)
Place of Event: Tübingen, Germany
Start-/End Date: 2004-01-30 - 2004-02-01

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Title: 7th Tübingen Perception Conference: TWK 2004
Source Genre: Proceedings
 Creator(s):
Bülthoff, HH1, Editor            
Mallot, HA, Editor            
Ulrich, RD, Editor
Wichmann, FA1, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Kirchentellinsfurt, Germany : Knirsch
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 50 Identifier: ISBN: 3-927091-68-5