中国卒中杂志 ›› 2025, Vol. 20 ›› Issue (9): 1079-1086.DOI: 10.3969/j.issn.1673-5765.2025.09.002

• 专题论坛 • 上一篇    下一篇

混合脑机接口-功能性电刺激运动康复系统的构建与验证

王瑶1,李雨涵1,陈小刚2   

  1. 1 天津 300387 天津工业大学生命科学学院
    2 中国医学科学院北京协和医学院生物医学工程研究所
  • 收稿日期:2025-06-02 修回日期:2025-08-01 接受日期:2025-08-08 出版日期:2025-09-20 发布日期:2025-09-20
  • 通讯作者: 陈小刚 chenxg@bme.cams.cn
  • 基金资助:
    国家重点研发计划(2023YFF1205300;2022YFC3602803)
    国家自然科学基金(62471495;62171473)
    天津市科技计划项目(24JCJQJC00040;24JCZDJC00430)

Construction and Validation of the Hybrid Brain-Computer Interface-Functional Electrical Stimulation Motor Rehabilitation System

WANG Yao1, LI Yuhan1, CHEN Xiaogang2   

  1. 1 School of Life Sciences, Tiangong University, Tianjin 300387, China
    2 Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China
  • Received:2025-06-02 Revised:2025-08-01 Accepted:2025-08-08 Online:2025-09-20 Published:2025-09-20
  • Contact: CHEN Xiaogang, E-mail: chenxg@bme.cams.cn

摘要: 目的 通过融合运动想象(motor imagery,MI)与周围视野稳态视觉诱发电位(steady-state visual evoked potential,SSVEP),构建混合脑机接口(brain-computer interface,BCI)-功能性电刺激(functional electrical stimulation,FES)运动康复系统,为卒中患者手部运动功能恢复提供新型神经康复手段。
方法 选取符合纳入标准的健康受试者,构建混合BCI-FES运动康复系统。实验过程中,在中央视野呈现手部抓握视频以引导受试者进行手部MI,同时在周围视野呈现高频视觉闪烁以诱发SSVEP。采用滤波器组公共空间模式和扩展型任务相关成分分析算法分别检测MI和SSVEP信号,并通过基于分类概率和相关系数的融合策略确定视觉任务的最终分类结果,进而触发FES实现手部运动。
结果 研究共纳入11名健康受试者,包括5名女性和6名男性,平均年龄为(24.3±1.6)岁。健康受试者的在线实验结果显示,BCI-FES运动康复系统的平均分类准确率达到98.48%,显著高于传统单一范式的准确率。
结论 本研究构建了一种融合MI与SSVEP的BCI-FES运动康复系统,并验证了其可行性,为运动障碍患者的康复训练提供了创新方案。

文章导读: 融合MI和周围视野SSVEP的混合BCI-FES运动康复系统实现了高准确度FES控制,为运动障碍康复治疗提供了创新性的解决方案。

关键词: 脑机接口; 运动想象; 稳态视觉诱发电位; 功能性电刺激; 神经康复

Abstract: Objective  To provide a novel neurorehabilitation approach for hand motor function recovery in stroke patients, a hybrid brain-computer interface (BCI)-functional electrical stimulation (FES) motor rehabilitation system was developed by integrating motor imagery (MI) and peripheral steady-state visual evoked potential (SSVEP). 
Methods  Healthy subjects who met the inclusion criteria were selected to construct a hybrid BCI-FES motor rehabilitation system. During the experiment, hand-grasping videos were displayed in the central visual field to guide subjects in performing hand MI, while high-frequency visual flickers were presented in the peripheral visual field to elicit SSVEP. The filter bank common spatial pattern and extended task-related component analysis algorithms were employed to detect MI and SSVEP signals, respectively. A fusion strategy based on classification probability and correlation coefficient was used to determine the final classification result, which subsequently triggered FES to induce hand movement. 
Results  A total of 11 healthy subjects were enrolled in the study, including 5 females and 6 males, with a mean age of (24.3±1.6) years. Online experimental results from healthy subjects demonstrated that the average classification accuracy of the BCI-FES motor rehabilitation system reached 98.48%, significantly outperforming traditional single-paradigm approaches. 
Conclusions  This study developed a BCI-FES motor rehabilitation system integrating MI and SSVEP, validated its feasibility, and provided an innovative solution for rehabilitation training in patients with motor dysfunction.

Key words: Brain-computer interface; Motor imagery; Steady-state visual evoked potential; Functional electrical stimulation; Neurorehabilitation

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