基于脑肌电混合脑机接口的中风康复系统设计与实现

Stroke Rehabilitation System based on EEG-EMG Hybrid Brain-Computer Interface

脑机接口可以帮助人们实现用思维活动控制外部设备,常用于中风患者康复训练中.然而,传统的脑机接口系统往往忽视了运动执行的序贯信息和大脑与肌肉的关联.本文通过引入肌电这一动作执行的直接体现,设计了一个基于脑肌电混合脑机接口的中风康复系统.该系统可以分析脑电和肌电信号的相关性数据,得到动作的序贯表示,并带动一个外置的机械手执行代偿运动.本研究涉及的系统主要由一个本地web应用Django来实现,可以方便中风患者在家用电脑上进行康复训练,并取得更好的康复效果.

Brain-computer interfaces(BCIs)enable people to control external devices by using their mental activities,and are commonly used in stroke rehabilitation.However,for stroke patients,traditional brain-computer interface systems overlook the sequential information of motor execution and the connection between the brain and muscles.Electromyography(EMG)is a physiological electrical signal that directly reflects the execution of movements.In this study,by introducing EMG into the brain-computer interface system,we designed and implemented a stroke rehabilitation system based on a brain-muscle hybrid brain-computer interface.This system can analyze the correlation data of electroencephalogram(EEG)and EMG,obtain the sequential representation of movements,and drive an external mechanical hand to perform compensatory movements.The system involved in this research is mainly implemented by using Django,which is a local web application that allows stroke patients to easily undergo rehabilitation training on their home computers and achieve better rehabilitation results.