《中国康复理论与实践》 ›› 2017, Vol. 23 ›› Issue (1): 4-9.doi: 10.3969/j.issn.1006-9771.2017.01.002

• 专题手功能康复 • 上一篇    下一篇

基于运动想象脑机接口的手功能康复系统设计

张桃, 杨帮华, 段凯文, 唐健真, 韩旭   

  1. 上海大学机电工程与自动化学院自动化系,上海市 200072。
  • 收稿日期:2016-04-27 出版日期:2017-01-20 发布日期:2017-02-17
  • 作者简介:张桃(1992-),女,汉族,江苏江阴市人,硕士研究生,主要研究方向:脑机接口与脑卒中后手功能康复。通讯作者:杨帮华,女,博士,研究员。E-mail: yangbanghua@shu.edu.cn。
  • 基金资助:
    国家自然科学基金青年基金项目(No.31100709)

Development of Hand Function Rehabilitation System Based on Motor Imagery Brain-computer Interface

ZHANG Tao, YANG Bang-hua, DUAN Kai-wen, TANG Jian-zhen, HAN Xu   

  1. Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China
  • Received:2016-04-27 Published:2017-01-20 Online:2017-02-17
  • Contact: YANG Bang-hua. E-mail: yangbanghua@shu.edu.cn

摘要: 针对脑卒中患者的手功能康复,本文介绍一种基于运动想象脑机接口的康复系统。该系统包含3个子系统。离线训练子系统依次呈现黑屏、左/右手动作视频、左/右箭头,分别提示患者休息、准备并指导运动想象、进行运动想象任务,最终将采集的脑电信号(EEG)建立识别模型;在线更新训练子系统依次呈现黑屏、箭头提示,依据建立的模型分析患者EEG信号,控制手部动作视频呈现,同时反馈给患者产生更易识别的EEG信号,建立更有效的识别模型;虚拟现实在线训练子系统在3Dmax中制作训练场景模型、3D人物模型及其手部动画,导入Unity3D中设计控制方式,患者通过手部运动想象,依据有效模型分析其EEG信号,实时控制3D人物手部运动。该系统具有多层次、沉浸感强等特点,为手功能康复提供新方法,同时为家庭化训练奠定基础。

关键词: 脑机接口, 运动想象, 脑卒中, 手功能, 康复, 虚拟现实

Abstract: This paper introduced a hand function rehabilitation system based on motor imagery (MI) brain-computer interface for hand function rehabilitation of stroke patients. The rehabilitation system contains three subsystems. Offline training subsystem displays the blank screen, a left or right hand movement video and arrow in turn, which respectively reminders patients to rest and make preparations for MI and instruct them how to do MI, and be doing MI. Finally, the patients' electroephalography (EEG) signals are acquired and processed to generate a recognition model. Model update online training subsystem presents the black screen and a left or right arrow, the meanings of which are the same as those in offline training subsystem. Then the acquired EEG signals are analyzed according to the established recognition model. Next, the analysis result is derived to control the hand movement video to be played. The video can also act as a visual feedback, which makes patients' EEG signals easier to be recognized. The updated and more effective recognition model is built at last. Virtual reality (VR) online training subsystem constructs 3D grid models of VR scene, a 3D man model and its hand animations in the 3Dmax. Then, all of them are imported into Unity3D. The control methods of the animations are also designed in Unity3D. In the end, the patients' EEG signals are analyzed according to the updated recognition model, thus controlling the hand movements of the 3D man in real time. The developed system has many characteristics, such as multilevel training and more immersion, which hopefully promotes the plasticity of central nervous system. The designed system provides new treatments for post-stroke hand function rehabilitation and further lays the foundation for family-mode rehabilitation.

Key words: brain-computer interface, motor imagery, stroke, hand function, rehabilitation, virtual reality

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