基于脑电特征的多模式想象动作识别

Multi-Pattern Motor Imagery Recognition Based on EEG Features

对不同部位肢体想象动作诱发的脑电特征进行辨识,并提取出对应的思维信息,这是实现脑-机交互的经典方法之一,传统的左、右手双想象动作诱发模式下信息转化效率较低,引入多个肢体部位想象动作的多模式转化方法可望改善这一缺点.采用二维时频分析结合Fisher分析的方法,从典型受试者的多模式想象动作脑电信号中提取出有利于分类识别的事件相关去同步化和同步化特征信息,再使用支持向量机建立双层分类器对多模式想象动作进行分类识别.本方法对于4种不同肢体部位的识别可以达到较高的正确率(85.71%).结果表明,多模式想象动作的诱发脑电特征信息具有明显的空间特异性,可以用于脑-机交互思维任务的识别和提取,值得进一步研究.

Motor imagery is a classical method in brain-computer interaction, in which EEG features evoked by imaginary body movements are recognized and thought information is extracted. However, the traditional dual pattern with left/right hand has low efficiency of information transformation. In this paper we introduce a multi-pattern method of transformation to overcome this disadvantage. The method of two-dimensional time-frequency analysis combined with Fisher analysis was introduced to extract feature information of event related desynchronization/synchronization from multi-pattern imaginary movement evoked EEG signals of typical subjects. Then the support vector machine was used to establish double layers classifiers to identify and classify multi-pattern motor imagery. This method achieved an accuracy of 85.71% on recognition of imaginary movements of four different body parts. The result shows that the evoked EEG feature information of multi-pattern imaginary movements has obvious space specificity which can be used to recognize and extract thought information in brain-computer interaction. Therefore, it is worthy of further investigation.