NDLI: Classification of Three-Class Motor Imagery EEG Data by Combining Wavelet Packet Decomposition and Common Spatial Pattern

Content Provider	IEEE Xplore Digital Library
Author	Wei Tu Qingguo Wei
Copyright Year	2009
Abstract	Low information transfer rate is inherent in a binary brain-computer interface (BCI) and largely limits its practical application. To increase information transfer speed, it is necessary to put emphasis on the research of multi-task BCIs. This paper proposes a new algorithm for classifying single-trial motor imagery EEG data in a three-task BCI. Wavelet packet decomposition (WPD) and common spatial pattern (CSP) are respectively applied to lowpass (0-64Hz) and bandpass (8-30Hz) filtered data to extract discriminative features. The two feature vectors are reduced to two dimensions by Fisher discriminant analysis (FDA) that is followed by a support vector machine (SVM) for classification. The algorithm was applied to three datasets recorded during BCI experiments of three-class motor imagery tasks. The classification accuracies for these three datasets range from 95.6% to 88.1% and their mean is 90.6%. The results verify the feasibility and validity of the algorithm.
Starting Page	188
Ending Page	191
File Size	260983
Page Count	4
File Format	PDF
ISBN	9780769537528
DOI	10.1109/IHMSC.2009.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Common spatial pattern Wavelet packet decomposition Electroencephalography Data mining Support vector machines Brain-computer interface Support vector machine classification Frequency Feature extraction Wavelet packets Man machine systems Intelligent systems Brain computer interfaces
Content Type	Text
Resource Type	Article

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