基于ITD和PLV的四类运动想象脑电分类方法研究

Research on four-class motor imagery EEG classification method based on ITD and PLV

针对四类运动想象任务的特征提取问题,提出一种基于固有时间尺度分解(ITD)和相位同步分析相结合的脑电(EEG)信号特征提取方法。采用第3届和第4届BCI竞赛中的4类运动想象数据集,首先选择5个导联的运动想象脑电信号,根据相位同步性计算导联之间的相锁值(PLV),将相锁值作为一类特征;之后利用ITD对5个导联的运动想象脑电信号进行分解,提取第一层固有旋转分量的能量特征,与PLV特征相结合获得十五维特征向量;最后通过支持向量机(SVM)进行分类识别。对12名受试者的平均识别率达到91.64%,平均Kappa系数达到0.887,说明该方法能够有效的提取脑电信号特征,进而提高4类运动想象任务的分类准确率。

Aiming at the feature extraction issue of four class motor imagerytask, this paper proposes an EEG signal feature extraction method based on intrinsic time-scale decomposition (ITD) and phase synchronization analysis.The four-class motorimagery datasets from the BCI Competition III and BCI Competition IV are adopted. Firstly, this method selects five channel motorimagery EEG (electroencephalogram) signals, calculates the phase locking value ( PLV) among the channels according to the phase synchronization, and uses the PLV as a kind of feature. Then, ITD is used to decompose the five channel motorimagery EEG signals and extract the energy feature of the first layer proper rotation component ( PRC), which is combined with the PLV feature to obtain the fifteen-dimensional feature vector. Finally, support vector machine ( SVM) is used for classification recognition. The average recognition rate and Kappa coefficient for 12 subjects reach 91. 64% and 0. 887 Respectively. The results show that this method can effectively extract the feature of EEG signals and improve the classification accuracy of four-class motor imagery task.