多通道三维视觉指导运动想象脑电信号特征选择算法

oncern the problem that multi-channel Motor Imagery (MI) of Brain-Computer Interface (BCI) based on 3D visual guidance with more redundancy information and poor classification accuracy, this paper proposed a pattern classification method based on wavelet packet decomposition(WPD)-common spatial pattern(CSP)-adaptive differential evolution(ADE) for feature extraction of electroencephalogramEEG.Firstly, this algorithm used WPD to divide the multi-channel motion imagery EEG signals into fine sub-bands. Secondly, it used CSP to obtain the eigenvectors corresponding to each subspace of WPD transformation. Finally, it selected the feature vectors through the ADE algorithm to obtain the best feature subsets for classification. Using WPD-CSP-ADE mode for feature extraction and selection, it had better performance in classification accuracy and number of features than the classic WPD-CSP method. At the same time, the classification performance of the proposed algorithm was significantly better than the genetic algorithm and particle swarm optimization algorithm. The experiments show that the WPD-CSP-ADE method can effectively improve the classification accuracy and reduce the number of features used for classification.