PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Support Vector Channel Selection in BCI
Thomas Navin Lal, M. Schroeder, T. Hinterberger, J. Weston, M. Bogdan, N. Birbaumer and Bernhard Schölkopf
IEEE Transactions on Biomedical Engineering Volume 51, Number 6, pp. 1003-1010, 2004.

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 and Zero-Norm Optimization which are based on the training of Support Vector Machines (SVM). These algorithms can provide more accurate solutions than standard filter methods for feature selection. 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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:400
Deposited By:Thomas Navin Lal
Deposited On:19 December 2004