基于最小二乘支持向量机的脑电信号分类

摘要 研究了基于运动想象脑电信号对大脑的想象运动状态进行分类识别的问题.根据事件相关同步和事件相关去同步现象识别出被试的想象运动状态，通过频带能量特征提取方法获得了想象左右手运动时的脑电信号特征，使用最小二乘支持向量机对提取到的频带能量特征进行分类.结果表明，使用最小二乘支持向量机可以对运动想象脑电信号的频带能量特征进行有效分类，分类正确率达到92%，其分类效果与使用标准支持向量机相当，但在计算速度上更有优势.
Abstract：The classification of mental states based on motor imagery(MI) electroencephalograph(EEG) signal was investigated. All the states were classified according to the phenomenon of event-related synchronization and event-related desynchronization. The band power of the MI EEG signal was extracted as the input feature and then classified by using LS-SVM. The final classification accuracy is 92%， which shows that LS-SVM performs well for the classification of the band power feature of MI EEG signal. And compared to the standard SVM， the performance of LS-SVM is as good as that of the standard SVM， but has some advantage in computing time.