摘要： 针对运动想象脑电信号的识别问题,提出一种改进的脑电信号特征提取与分类方法。利用局部均值分解算法将原始信号分解为一系列乘积函数(PF)分量,根据μ节律和β节律范围内的脑电信号剔除无意义的PF分量。通过特征时间选择原则,选取4 s~6 s运动想象脑电信号作为分类数据,分别计算C3,C4导联信号二阶和三阶PF分量样本熵的和,并将其均值MSampEn(C3,C4)作为输入元素构造脑电特征向量,利用支持向量机进行分类预测以识别左右手想象运动。实验结果表明,与经验模态分解以及总体经验模态分解方法相比,该特征提取方法具有更高的分类准确率。

关键词: 脑机接口, 特征提取, 局部均值分解, 运动想象, 样本熵, 支持向量机

Abstract: Aiming at identification problem of motor imagery Electroencephalogram(EEG) signals,this paper proposes an improved feature extraction and classification method of EEG signals.The original signal is decomposed into a series of Product Function(PF) components by Local Mean Decomposition(LMD) and meaningless PFs are eliminated according to the EEG signals within the scope of μ rhythm and β rhythm.With the principle of characteristic time selection,motor imagery signals of 4 s~6 s are selected as classification data.Then the sum of sample entropy for the second and third-order PFs of C3 and C4 lead signals are calculated respectively and their mean values MSampEn(C3,C4) can be used as elements of EEG feature vector,which is classified with Support Vector Machine(SVM) to recognize imagery movements.Experimental results indicate that the proposed feature extraction method which has higher classification accuracy than Empirical Mode Decomposition(EMD) and Ensemble Empirical Mode Function(EEMD) method.

Key words: Brain-Computer Interface(BCI), feature extraction, Local Mean Decomposition(LMD), motor imagery, sample entropy, Support Vector Machine(SVM)

中图分类号: 

• TP18