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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: https://hdl.handle.net/11858/00-001M-0000-0013-D9E5-7 Version Permalink: https://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: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 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