Energy Entropy Used in Identification Based on Motor Imagery EEG

Abstract:

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Subjects are identified by classifying motor imagery EEG signal. Energy entropy was used to preprocess motor imagery EEG data, and the Fisher class separability criterion was applied to extract features. Finally, classification of of extracted features was performed by a Linear discrimination analysis method. Four types motor imagery EEG of three subjects was classified respectively. The results showed that the average classification accuracy achieved over 85%, and the highest was 88.7% on tongue movement imagery EEG

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Periodical:

Edited by:

Jerry Tian

Pages:

274-278

DOI:

10.4028/www.scientific.net/AMR.304.274

Citation:

X. Dan "Energy Entropy Used in Identification Based on Motor Imagery EEG", Advanced Materials Research, Vol. 304, pp. 274-278, 2011

Online since:

July 2011

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$35.00

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