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Indexed by:会议论文

Date of Publication:2013-07-03

Included Journals:EI、CPCI-S、Scopus

Page Number:7052-7055

Abstract:For the dynamic classification of motor imagery mind states in the brain-computer interface (BCI), we propose a power projection based feature extraction method to classify the electroencephalogram (EEG) signals by combining information accumulative posterior Bayesian approach. This method improves the classification accuracy by maximizing the average projection energy difference of the two types of signals. The experimental results on two BCI competition datasets show that the classification accuracy is about 90%. The results of the classification accuracy and mutual information demonstrate the effectiveness of this method.