多任务运动想象脑电特征的融合分类研究

针对运动想象脑-机交互任务模式单一、识别精度低、实用性较差等问题，采用改进的共空间模式（CSP）的特征提取方法，并利用支持向量机（SVM）与CSP融合分类方法对多类任务运动想象脑电特征进行分类识别。首先，选择特定导联上的脑电信号进行小波分解与重构，去除冗余信息；其次，利用特征参数做差的方法，得到较为明显的脑电特征；最后，通过SVM融合CSP的分类模式，对脑电特征进行多任务分类。利用BCI竞赛数据，对左手，右手，舌和脚四类运动想象任务的脑电进行识别。结果表明：分类正确率最高达到90.9%，平均正确率为86.8%，Kappa系数为0.8867，信息传输速率可达0.68 bit/trial，能够有效的获得脑电特征并较好的实现多任务运动想象脑电识别。

Research of fusion classification of EEG features for multi-class motor imagery

In view of the problems of pattern simplification,low accuracy of classification and poor practicability in motor imagery BCI,they improve feature extraction method to common spatial pattern(CSP),and the support vector machine(SVM)is used to carry out multi-class classification,combining with the CSP to classify the feature signal of EEG.Firstly,they select EEG signal in the specific channel to do wavelet decomposition and reconstruction,in or?der to remove redundant information;Secondly,they improve the method,by doing subtractions between different characteristic parameters,and obtain obvious characteristics of EEG;Finally,the SVM is used to carry out multi-task classification,combining with the CSP to classify the feature signal of EEG. Using BCI competition data,the four kinds of motor imagery tasks of left hand,right hand,tongue and feet are identified based on EEG signals. Ex?perimental results show that the correct rate of classifying is 90.9% for maximum,the average accuracy rate is 86.4%,the Kappa coefficient is 0.8867,and the information transmission rate was 0.68bit/trial and the method can extract EEG features effectively and achieve better classification to a multi-task motor imagery of EEG signals.