基于概率协作表示的运动想象脑电分类算法

doi:10.3976/j.issn.1002-4026.2018.02.017

Abstract

Abstract:

In the research of brain-computer interface, a classification method for recognizing the features of motor imagery EEG signals based on probabilistic collaborative representation (ProCRC) was proposed in this paper. The maximum likelihood that a test sample belonged to each of the multiple classes was compared, so as to determine the final classification that it belonged to.Performance of this method was tested using the data set of BCI competition Ⅲ. Firstly, the S transform was used to extract the electroencephalography features, and then different classifiers were compared. Finally, the classification accuracy was used as the evaluation criterion to verify the effectiveness of the algorithm. The accuracy of the algorithm proposed in this paper could reach 90%, which provided a new idea for the research of the classification algorithm of the braincomputer interface system.

Key words: probabilistic collaborative representation, motor imagery, S-transform, brain-computer interface

CLC Number:

TP302.7

CUI Li-xia, YANG Ji-min,CHANG Hong-li. Motor imagery EEG classification algorithm based on probabilistic collaboration representation[J].SHANDONG SCIENCE, 2018, 31(2): 105-112.

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Motor imagery EEG classification algorithm based on probabilistic collaboration representation

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