NDLI: Stepwise Feature Selection by Cross Validation for EEG-based Brain Computer Interface

Content Provider	IEEE Xplore Digital Library
Author	Tanaka, K. Kurita, T. Meyer, F. Berthouze, L. Kawabe, T.
Copyright Year	2006
Description	Author affiliation: Univ. of Tsukuba, Ibaraki (Tanaka, K.)
Abstract	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.
Starting Page	4672
Ending Page	4677
File Size	316838
Page Count	6
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247119
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain computer interfaces Support vector machines Support vector machine classification Electroencephalography Kernel Electrodes Signal processing Fasteners Communication system control Spinal cord injury
Content Type	Text
Resource Type	Article

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