基于支持向量机和PCA的脑电α波运动想象分类研究  

Classification of α wave motor imagery based on SVM and PCA

作　　者：蔡靖[1] 刘光达[1] 王尧尧 宫晓宇[2] Cai Jing;Liu Guangda;Wang Yaoyao;Gong Xiaoyu(College of Instrumentation&Electrical Engineering,Jilin University,Changchun 130012,China;Educational Technology Center,Jilin University,Changchun 130061,China)

机构地区：[1]吉林大学仪器科学与电气工程学院,吉林长春130012 [2]吉林大学教育技术中心,吉林长春130061

出　　处：《电子技术应用》2022年第6期23-27,共5页Application of Electronic Technique

摘　　要：针对脑电信号(EEG)运动想象分类过程中弱相关特征量影响分类准确度的问题,提出一种筛选方法,该方法是基于α波和主成分分析(PCA)算法的。基于脑机接口(BCI)系统,通过听觉诱发刺激产生向左和向右两种运动想象任务对应的脑电信号,并对其做小波包分解处理,然后进行脑电α频段信号的重构,从而提取出α波形并对其进行统计特征提取。再结合PCA技术和支持向量机(SVM)方法,实现弱相关特征的剔除和特征分类。根据筛选后的数据进行分类,所得结果准确率更高,信号分类的准确度由90.1%提高至94.0%。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 theαband signals of the EEG were reconstructed,so as to extract theαwaveforms 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%.

关 键 词：小波包分解 支持向量机 运动想象 主成分分析 脑电信号 

分 类 号：TN911.7[电子电信—通信与信息系统] R318[电子电信—信息与通信工程]