基于改进CSP算法的运动想象脑电信号分类方法

Classification Method of Motor Imagery EEG Signal Based on Improved CSP Algorithm

针对传统共空间模式(CSP)算法处理运动想象脑电信号存在的分类正确率较低和算法实时性较差的问题,提出一种时-空-频域相结合的CSP脑电信号分析方法:首先利用小波包对EEG原始信号进行分解,根据EEG信号的频域分布提取运动想象脑电节律,通过改进CSP算法对脑电节律进行空间滤波来提取特征;然后通过引入时间窗对脑电信号进行时域滤波,消除运动想象开始和结束时脑电的波动;最后根据运动想象脑电信号在大脑皮层的生理分布特点,提出利用主轴通道的思想对脑电信号进行处理,分析不同情况下算法的计算时间和分类效果。实验结果显示:在主轴通道数为29和时间窗为2 s时,算法运行时间为1.562 s,比传统方法缩短67%,平均分类正确率达到97.5%,验证了该方法能够有效提高运动想象脑电信号的分类正确率和算法的实时性。

For the problem of low classification accuracy and poor real-time performance during the traditional common spatial patterns（CSP） algorithm for motor imagery EEG signal processing,a new analysis method of CSP EEG signal based on time space frequency domain is put forward.Firstly,the wavelet packet is used to decompose the original signal of EEG,the motor imagery EEG rhythm is extracted according to the frequency distribution of EEG signal,and the spatial features of EEG are extracted by improving CSP algorithm.Then,we introduce the time window to filter the EEG signals,and eliminate the influence of EEG fluctuation at the beginning and end of the motion imagery.Lastly,according to the characteristics of the physiological distribution of EEG signals in the brain cortex,the method based on spindle channel is used to process the EEG signal and analyze computational time of different algorithms and the classification results.The experimental results show that,the running time of the algorithm is 1.562 s,which is 67% shorter than the traditional method,and the average classification accuracy is up to 97.5% when the number of spindle channels is 29 and the time window is 2 s.In the meantime,the results show that the proposed method can effectively improve the classification accuracy and the real-time performance of motor imagery EEG.