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  Support Vector Channel Selection in BCI

Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., et al.(2003). Support Vector Channel Selection in BCI (120). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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MPIK-TR-120.pdf (Publisher version), 2MB
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Lal, TN1, 2, Author              
Schröder, M, Author              
Hinterberger, T, Author
Weston, J1, 2, Author              
Bogdan , M, Author
Birbaumer, N, Author
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying EEG signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination [3] and Zero-Norm Optimization [13] which are based on the training of Support Vector Machines (SVM) [11]. These algorithms can provide more accurate solutions than standard filter methods for feature selection [14]. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.

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 Dates: 2003-12
 Publication Status: Published in print
 Pages: 9
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Rev. Type: -
 Identifiers: Report Nr.: 120
BibTex Citekey: 2482
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 120 Sequence Number: - Start / End Page: - Identifier: -