异步BCI系统中运动想象诱发脑电特征识别

针对异步运动想象脑机交互（Brain Computer Interface，BCI）系统中空闲状态检测和不同想象任务分类的问题，在小波变换提取脑电信号特征基础上，设计了阈值判别结合支持向量机的二级分类器。由于大脑想象单侧肢体运动时，会导致同侧和对侧运动皮层脑区EEG信号在μ节律上分别出现事件相关同步和去同步，而大脑处于空闲状态时则无此现象。基于大脑活动的这一特性，提出了小波能量阈值判别法，进行空闲状态检测，径向基核函数和交叉检验的支持向量机方法，进行左、右手运动想象任务分类。结果表明该分类器最佳分类正确率达到了80.7%，且整个时间消耗仅为3.0 s，可以较好地满足异步在线运动想象BCI系统的应用。

Identification of EEG feature evoked by motor imagery in asynchronous BCI system

A two-level structure classifier is designed aiming to solve the problems, idle state detection and classification of different motor imagery tasks, in an asynchronous BCI system. The classifier integrates a threshold discrimination method with a Support Vector Machine, on the basis of EEG feature extraction by wavelet transform. When everyone imagines unilateral limb movement in the brain, μ rhythm on EEG signal will represent ERS and ERD phenomenon in its ipsilateral and contralateral motor cortex areas. However, there is no such phenomenon during the idle state of the brain. Based on this characteristic, this paper presents a wavelet energy threshold discrimination approach to detect the idle state of the brain. Subsequently, Support Vector Machine, based on the RBF kernel function and the cross-validation method, is utilized to classify the left and right hand motor imagery tasks. Experimental results show that the accuracy of the optimal task classification can reach 80.7% costing only about 3.0 s. Therefore, it is appropriate to the proposed classifier applied in an asynchronous real-time motor imagery brain-computer interface system.