运动想象脑电信号识别研究

通过对运动想象脑电信号的分类，对受试者进行身份识别。采用一种盲源分离算法——二阶盲辨识对运动想象脑电信号进行处理，提高运动想象脑电信号的信噪比，进而采用Fisher距离对处理后的信号进行特征提取，最后采用BP神经网络对特征集进行分类，从而实现对受试者的身份识别。对3位受试者的4类运动想象脑电信号分别进行了分类识别，结果显示，4类运动想象脑电信号的识别率均达到80％左右，其中最高的是想象舌动脑电信号，其识别率达到88.1％，这在类似研究中属于较高的水平。

Identification of motor imagery EEG signal

Subjects are identified by classifying motor imagery EEG signal.Second-Order Blind Identification（SOBI）,a Blind Source Separation（BSS） algorithm is applied to preprocess EEG data for higher signal-to-noise ratio.Subsequently,Fisher distance is used to extract features.Finally,classification of extracted features is performed by back-propagation neural networks. Four types motor imagery EEG of three subjects is classified respectively.The results show that the average classification accuracy achieves over 80%,and the highest is 88.1% on tongue movement imagery EEG.