基于改进差分进化算法的运动想象脑机接口频带选择

doi:10.3969/j.issn.1000-1158.2018.02.28

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Abstract Due to the fact that different peoples brain response to events is in different frequency bands, a method of multi-strategy mutation operator and time-varying nonlinear crossover factor differential evolution is proposed for accurate determination of the personal optimal filtering frequency band, and feature vectors are extracted by common spatial pattern algorithm and classified by the linear classifier. Based on this strategy, 10 times 5-fold cross validation experiments for BCI competition III-dataset 4a EEG data of five subjects are implemented. Experimental results show that the algorithm has the advantages of strong stability, less time-consuming and strong real-time performance, and the problem of optimal bands selection in motor imagery BCI feature extraction is therefore solved.

Key words： metrology band selection differential evolution brain-computer interface motor imagery

Received: 10 October 2016 Published: 11 February 2018

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TB973

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	Articles by authors
	HU Chun-hai
	LI Tao
	LIU Yong-hong
	QI Fan

Cite this article:

HU Chun-hai,LI Tao,LIU Yong-hong, et al. Motor Imagrey Brain Computer Interface Band Selection Based on Improved Differential Evolution Algorithm[J]. Acta Metrologica Sinica, 2018, 39(2): 276-279.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2018.02.28 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2018/V39/I2/276

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