Abstract：Due to the fact that different peoples 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.
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2018, Vol. 39 
