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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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Lal, TN1, 2, Autor           
Schröder, M, Autor           
Hinterberger, T, Autor
Weston, J1, 2, Autor           
Bogdan , M, Autor
Birbaumer, N, Autor
Schölkopf, B1, 2, Autor           
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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 Zusammenfassung: 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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 Datum: 2003-12
 Publikationsstatus: Erschienen
 Seiten: 9
 Ort, Verlag, Ausgabe: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifikatoren: Reportnr.: 120
BibTex Citekey: 2482
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Titel: Technical Report of the Max Planck Institute for Biological Cybernetics
Genre der Quelle: Reihe
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Seiten: - Band / Heft: 120 Artikelnummer: - Start- / Endseite: - Identifikator: -