基于TRCSP和L2范数的脑电通道选择方法

EEG channel selection method based on TRCSP and L2 norm

脑-机接口(BCI)系统常用高密度电极通道来获取较高空间分辨率的脑电(EEG)信号,但同时也会引入过多的噪声通道,影响脑电的解码性能。为了消除无关的噪声通道,提出了一种基于Tikhonov正则化共空间模式(TRCSP)和L2范数的运动想象脑电通道选择方法。首先基于TRCSP和分类器得到最优的空间滤波器,接着基于L2范数对空间滤波器得到的各通道的权重值进行排序。选择前K个通道的数据进行CSP特征提取,根据分类器的分类准确率确定最优K值,进而得到最优的通道数和通道组合。在实验中,使用6种分类器分别在BCI竞赛III(2005)数据集IVa和实验室自采集数据上验证所提出的通道选择方法的有效性。所提出的方法在两个数据集上的平均分类准确率分别达到了87.57%和74.32%,优于其它现有的方法。

High-density electrode channels are commonly used in brain-computer interface(BCI) systems to obtain high spatial resolution EEG signals,but at the same time,too many noise channels are introduced,which affect the decoding performance of electroencephalogram(EEG).In order to eliminate irrelevant noise channels,a channel selection method based on Tikhonov regularized co-spatial pattern(TRCSP) and L2 norm for motor imagery EEG is proposed this paper.Firstly,the optimal spatial filter is obtained based on TRCSP and classifier,and then the weight values of each channel obtained by the spatial filter are sorted based on L2 norm.The data of the first K channels are used for CSP feature extraction,and the optimal K value is determined according to the classification accuracy of the classifier,so as to obtain the optimal number of channels and channel combination.In the experiment,six classifiers were used on the BCI competition III(2005) dataset IVa and self-collected dataset from our laboratory to verify the effectiveness of the proposed channel selection method.The average classification accuracy of the proposed method on the two datasets reached 87.57% and 74.32%,respectively,better than other existing methods.