基于脑电和眼电的运动想象多尺度识别方法研究

Research on EEG and EOG Based Multiscale Recognization Method of Motor Imagery

基于脑电信号对同一肢体不同动作想象模式进行识别的正确率低,已成为基于脑机接口对肢体瘫痪患者进行运动想象训练监控的方法,获得临床应用前必须解决的瓶颈问题.针对该问题,本文提出一种利用运动想象时眼睛的活动状态与所想象肢体动作之间存在的耦合关系,进行运动想象多尺度识别的新方法.该方法首先在大尺度上,利用脑电信号对运动想象是否发生进行识别,再结合同一运动想象过程眼电信号协同变化模式的识别结果,基于决策融合在更精细的尺度上,对同一肢体不同动作的想象模式进行识别.实验结果表明,仅基于脑电进行右臂三种动作想象模式识别的平均正确率为63.0%,而应用所提出方法可以将其提高到91.4%.所提出方法可望有临床应用前景.

The accuracy on electroencephalogram(EEG)based motor imagery recognization of different movements of a same limb is low.It constrains the monitor method,which monitor motor imagery training of patients with limb paralysis based on brain computer interface,to be used in clinical.Aiming at the problem,in this paper a new method of multiscale recognization of motor imagery is proposed.It is based on the coupling relationship between eye movement and imagined movement during motor imagery.In this method,first of all EEG is applied to judge whether motor imagery occurs on a large scale,and then combined with the recognization result of electrooculogram(EOG),which changes cooperatively with EEG during the same motor imagery period,the different movements of a same limb is recognized using decision fusion on a finer scale.The results of experiments reveal that the average recognization accuracy of three kinds of movement imagery of the right arm can be promoted from 63.0%(only with EEG)to 91.4%(with the proposed method).The proposed method may be applied in clinical in the future.