基于ERS/ERD的二级共空间模式的运动想象脑电信号特征提取

Feature extraction of motor imagery EEG of two layers common spatial pattern based on ERS/ERD

针对多类运动想象EEG信号在脑-机接口方面存在分类识别率低和被试者差异性的问题,提出了一种基于ERS/ERD现象的二级共空间模式特征提取的方法。首先对全部导联进行特定频段的小波包降噪和分解;其次对分解系数重构后的信号以手(左、右)和脚(脚、舌)这二类进行一级共空间模式获取空间滤波器并对其采用2-范数筛选准则,提取权重系数较大的N个导联;然后以优化导联的投影矩阵对手与脚进行空间滤波后的信号分别作为原始信号进行二级空间模式特征提取;最后采用支持向量机进行分类。采用BCI2005Ⅲa中三位被试者的数据进行仿真验证,得到分类正确率最高达到92.55%。结果表明,该方法对EEG信号的特征提取具有较好的效果。

In order to solve the problem that the classification recognition rate of the multi-class motor imagery EEG signals is low and that there is difference among subjects in the brain-computer interface, we propose a feature extraction method based on the ERS/ERD phenomenon for the two level common spatial pattern. Firstly, we select the EEG of all channels and make wavelet packet de-noising and decomposition （WPD） of specific frequency bands. Secondly, we conduct common spatial pattern （CSP） on the signals of reconstructed decomposition coefficients to obtain spatial filtering devices for the two classes of hands （left, right） and feet （feet, tongue）, and use the 2-norm screening criteria to extract N leads of the heavily weighted factor. Thirdly, the projection matrix of the optimized leads are used to filter hands （left, right） and feet （feet, tongue）, and the signals which are regard as the original signals are used to conduct two layers common spatial pattern. Finally, feature vectors are categorized by the support vector machine （SVM）. The highest classification accuracy rate of the simulation on three subjects from BCI2005IIIa reaches 92.55%, and the simulation results show that the proposal has a good effect on the feature extraction of EEG signals.