摘要
目的对脑控主被动协同刺激康复训练关键技术进行研究,探索解决脑卒中患者在康复训练过程中主动参与程度较低的问题。方法探索一种全新的脑控主被动协同刺激康复训练方法,集成了脑机接口、稳态运动视觉诱发电位、虚拟现实和下肢康复训练机器人等技术,通过视觉刺激效果和被动训练作用于患者中枢神经,形成信息传递的闭环回路,实现运动神经通道的协同刺激,并搭建了脑控主被动协同刺激的下肢康复训练系统。结果所有被试者都在本系统的辅助下顺利完成了实验,检测程序在信息传输率为6.82～16.11bits/min时,系统检测的准确度为76.7%～96.7%,系统识别被试者运动意图的平均时间为6.01s,平均识别率为82.8%。结论本系统通过脑控主被动协同训练,提高了患者的训练效率和积极主动性,具有良好的应用前景。
Objective To study the key techniques of rehabilitation training of EEG-driven active and passive co-stimulation(APC)to solve the problem of low active participation in rehabilitation training of stroke patients.Methods We explored a new EEG-driven APC rehabilitation training method by integrating the brain-computer interface(BCI),steady-state motion visual evoked potential(SSMVEP),virtual reality(VR),and lower limb rehabilitation training robot technologies.Patients'central nervous system received visual stimulation effect and passive effect to form information transfer of closed loop circuit,realize the co-stimulation of motor nerve channel,and set the EEG-driven active and passive co-stimulation of the lower limb rehabilitation training system(LLRTS).Results All the subjects completed the experiment under the assistance of the system.When the information transfer rate was from 6.82 to 16.11 bits/min during the testing procedure,the system detection accuracy ranged from 76.7%to 96.7%,the average time for the system to identify the subjects'motion intention was 6.01 s,and the average recognition rate was 82.8%.Conclusion This system can improve the training efficiency and initiative of patients through EEG-driven active and passive co-stimulation training,and has a good application prospect.
作者
李龙飞
曹飞帆
张鑫
梁仍昊
徐光华
LI Long-fei;CAO Fei-fan;ZHANG Xin;LIANG Reng-hao;XU Guang-hua(School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China)
出处
《西安交通大学学报（医学版）》
CAS
CSCD
北大核心
2019年第1期130-133,143,共5页
Journal of Xi’an Jiaotong University（Medical Sciences）
基金
国家重点研发计划(No.2017YFC1308500)
陕西省重点研发计划(No.2018ZDCXL-GY-06-01)~~
关键词
脑机接口
稳态运动视觉诱发电位
虚拟现实
下肢康复训练机器人
brain-computer interface(BCI)
steady-state motion visual evoked potential(SSMVEP)
virtual reality(VR)
lower limb rehabilitation training system(LLRTS)
