中图分类号： TN911.7；R318 文献标识码： A DOI：10.16157/j.issn.0258-7998.211723 中文引用格式： 蔡靖，刘光达，王尧尧，等. 基于支持向量机和PCA的脑电α波运动想象分类研究[J].电子技术应用，2022，48(6)：23-27. 英文引用格式： Cai Jing，Liu Guangda，Wang Yaoyao，et al. Classification of α wave motor imagery based on SVM and PCA[J]. Application of Electronic Technique，2022，48(6)：23-27.
Classification of α wave motor imagery based on SVM and PCA
Abstract： A feature screening method based on alpha wave and principal component analysis was proposed to solve the problem that the weakly correlated feature quantity would affect the classification accuracy in EEG motor imagery classification. Based on brain computer interface system, the EEG signals corresponding to left and right motor imagination tasks were generated by auditory stimulation and processed by wavelet packet decomposition, and then the α band signals of the EEG were reconstructed, so as to extract the α waveforms and extract the statistical features. Combined with PCA technology and SVM method, the weak correlation features are eliminated and classified. According to the selected data, the accuracy of the results is higher, and the accuracy of signal classification is improved from 90.1% to 94.0%.
Key words : wavelet packet decomposition；SVM；motor imagery；PCA；EEG