Stepwise feature selection by cross-validation for EEG-based Brain Computer Interface

Tanaka, K, Kurita, T, Meyer, F, Berthouze, L and Kawabe, T (2006) Stepwise feature selection by cross-validation for EEG-based Brain Computer Interface. In: Neural Networks 2006. IJCNN '06 International Joint Conference. IEEE Press, pp. 4672-4677. ISBN 0780394909

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The potential of brain-computer interfaces (BCI) in serving a useful purpose, e.g., supporting communication in paralyzed patients, hinges on the quality of the classification of the brain waves. This paper proposes a novel method to construct a classifier with improved generalization performance. A feature selection method is applied to features calculated from the EEG signals so that unnecessary or redundant features can be removed and only effective features are left for the classification task. Kernel support vector machines (kernel SVM) were used as a classifier and the best combinations of features were searched by backward stepwise selection, i.e., by eliminating unnecessary features one by one, and by evaluating the resulting generalization performance through cross validation. Experiments showed that the generalization performance of the classifier constructed from the best set of features was higher than that of the classifier using all features.

Item Type: Book Section
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Luc Berthouze
Date Deposited: 06 Feb 2012 18:14
Last Modified: 30 Nov 2012 16:59
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