基于稳态视觉诱发电位的脑电控制上肢康复机器人

针对现阶段基于脑机接口(brain-computer interface,BCI)的康复机器人存在多目标分类时间长、识别准确率仍有待提升的问题,设计了一种由脑电信号控制的上肢康复机器人,对脑电信号中的稳态视觉诱发电位(steady-state visual evoked po-tential,SSVEP)分类,进而判断出受试意图并输出相应动作指令.基于MATLAB的Psychtoolbox工具箱设计了包含5个刺激矩形的频闪界面作为视觉刺激器,刺激大脑生成SSVEP信号,对应上肢康复机器人的5个控制指令.运用多导联同步指数(multivariate synchronization index,MSI)算法对采集到的信号进行分类并输出控制指令,机器人在接收指令后执行特定动作.实验得到的机器人动作正确率最佳为98.33％,平均信息传输速率为23.11 bit/min.结果表明:SSVEP信号控制的上肢康复机器人在辅助治疗的方面具有良好的应用前景,可以有效提高肢体偏瘫患者的康复效果.

Control of upper limb rehabilitation robot based on steady-state visual evoked potential

In order to solve the problems of rehabilitation robots based on brain-computer interface (BCI) at present that long time of multi-target classification and recognition accuracy. This study was designed an upper limb rehabilitation robot controlled by EEG signals to classify the steady-state visual evoked potential (SSVEP) in the EEG signals, then judged the intention of the subject and output the corresponding action instruction. Based on MATLAB PsychToolbox a flicker interface with five stimulus rectangles was designed as a visual stimulator to stimulate the brain to generate SSVEP signals, which corresponding to the five control instructions of the upper limb rehabilitation robot. Multivariate synchronization index (MSI) algorithm was used to classify the collected signals and output control instructions, the robot performed specific action after receiving instruction. Through the experiment, the optimal accuracy of robot action and average information transmission rate are 98.33% and 23.11 bit/min, respectively. The results show that the upper limb rehabilitation robot controlled by SSVEP signal has a good application prospect in aided therapy, can effectively improve the rehabilitation effect of limb hemiplegia patients.