NDLI: Classifying EEG signals in Fisher discriminant spaces by random electrode selection

Content Provider	IEEE Xplore Digital Library
Author	Shiliang Sun
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai (Shiliang Sun)
Abstract	This paper introduces an ensemble approach for electroencephalogram (EEG) signal classification, which aims to overcome the instability of the Fisher discriminant feature extractor for brain-computer interface (BCI) applications. Through the random selection of electrodes from candidate electrodes, multiple individual classifiers are constructed. In a feature subspace determined by a couple of randomly selected electrodes, principal component analysis (PCA) is first used to implement dimensionality reduction. Successively Fisher discriminant is adopted for feature extraction, and a Bayesian classifier with a Gaussian mixture model (GMM) is trained to carry out classification. The outputs from all the individual classifiers are combined to give a final label. Experiments with real EEG signals taken from a BCI indicate the validity of the proposed random electrode selection (RES) approach.
Starting Page	2053
Ending Page	2056
File Size	271054
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4518044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Electrodes Pattern classification Principal component analysis Feature extraction Brain computer interfaces Space technology Brain modeling Sun Computer science random electrode selection (RES) EEG signal classification brain-computer interface (BCI) Fisher discriminant Gaussian mixture model (GMM)
Content Type	Text
Resource Type	Article

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