基于脑机接口的康复训练系统

doi:10.16182/j.issn1004731x.joss.18-0791

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (2): 174-180.doi: 10.16182/j.issn1004731x.joss.18-0791

基于脑机接口的康复训练系统

杨帮华^{1, 2}, 李博¹

1. 上海大学机电工程与自动化学院,上海 200444;
2. 上海市电站自动化重点实验室,上海 200444

收稿日期:2018-09-03 修回日期:2018-11-26 出版日期:2019-02-15 发布日期:2019-02-15
作者简介:杨帮华(1971-),女,河南三门峡,博士,研究员,研究方向为脑机接口,模式识别与智能系统;李博(1993-),男,河南项城,硕士生,研究方向为脑机接口。
基金资助:
国家科技部重点研发计划(2018YFC 1312903, 2018YFC0807405), 上海市科学技术委员会科技创新(18411952200)

Rehabilitation Training System Based on Brain Computer Interface

Yang Banghua^{1, 2}, Li Bo¹

1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China;
2. Key Laboratory of Power Station Automation Technology, Shanghai 200444, China

Received:2018-09-03 Revised:2018-11-26 Online:2019-02-15 Published:2019-02-15

摘要/Abstract

摘要： 脑机接口即大脑-计算机交互接口的简称,它是一种新的通信和控制方式。采用MATLAB与VC++混合编程的方法开发了基于脑机接口的脑卒中康复训练系统。该系统通过解码脑电信号能够实时、准确的分析出患者的运动意图,进而控制康复外设从视、听、触觉给予反馈,实现患者的主动康复。系统具有界面人性化、算法模块化、在线识别率高的特点,对基于BCI的应用开发具有借鉴意义。

关键词: 脑机接口, 康复训练, 混合编程, 动态链接库

Abstract: Brain-computer interface (BCI) is a new communication and control method. The BCI rehabilitation training system was developed for rehabilitation training of post-stroke patients by using MATLAB and VC++ hybrid programming method. By decoding the EEG signal, the patient's movement intention can be recognized accurately in real time, then the work of the rehabilitation peripheral is triggered to give feedback from the visual, auditory, and tactile. The patient's active rehabilitation is achieved through the system. The system has the characteristics of user-friendly interface, modular algorithm, high online recognition rate; and it can be used as a reference for BCI-based application development.

Key words: Brain-computer Interface, rehabilitation training, mixed programming, Electroencephalogram

中图分类号:

TP391.9

杨帮华, 李博. 基于脑机接口的康复训练系统[J]. 系统仿真学报, 2019, 31(2): 174-180.

Yang Banghua, Li Bo. Rehabilitation Training System Based on Brain Computer Interface[J]. Journal of System Simulation, 2019, 31(2): 174-180.

参考文献

[1] 程明, 高上凯, 张琳. 基于脑电信号的脑—计算机接口[J]. 北京生物医学工程, 2000, 19(2):113-118.
Cheng Ming, Gao Shangkai, Zhang Lin.Brain-computer Interface Based on EEG Signals[J]. Beijing Biomedical Engineering, 2000, 19(2): 113-118.
[2] 李明爱, 罗新勇, 崔燕, 等. 基于MI-BCI的上肢在线运动康复原型系统[J]. 北京生物医学工程, 2017, 36(3): 273-278.
Li Mingai, Luo Xinyong, Cui Yan, et al.MI-BCI based online prototype system for upper limb rehabilitation[J]. Beijing Biomedical Engineering, 2017, 36(3): 273-278.
[3] 马留洋. 基于虚拟现实的运动想象脑—机接口训练系统[D]. 郑州:郑州大学, 2017.
Ma Liuyang.Brain Computer Interface Training System Based on Virtual Reality[D]. Zhengzhou: Zhengzhou University, 2017.
[4] Cervera M A, Soekadar S R, Ushiba J, et al.Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis[J]. Annals of clinical and translational neurology (S2328-9503), 2018, 5(5): 651-663.
[5] Biasiucci A, Leeb R, Iturrate I, et al.Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke[J]. Nature Communications (S2041-1723), 2018, 9(1): 2421.
[6] Pfurtscheller G, Neuper C, Guger C, et al.Current trends in Graz brain-computer interface (BCI) research[J]. IEEE Transactions on Rehabilitation Engineering (S1063-6528), 2000, 8(2): 216-219.
[7] 陈颖萍. 基于小波和独立分量分析的脑电信号预处理研究[D]. 武汉:华中科技大学, 2006.
Chen Yingping.EEG Signal Preprocessing Study Based on Wavelet Transform and Independent Component Analysis[D]. Wuhan: Huazhong University of Science and Technology, 2006.
[8] 赵丽, 万柏坤, 高扬. 独立分量分析方法在脑电信号预处理中的应用研究[J]. 仪器仪表学报, 2003, 24(增1): 496-498.
Zhao Li, Wan Baikun, Gao Yang.The Applying Research of Independent Component Analysis on the preprocessind of EEG[J]. Journal of Scientific Instrument, 2003, 24(S1): 496-498.
[9] 万柏坤, 周仲兴, 刘亚伟, 等. 基于ICA与PSD的ERD/ERS信号检测[J]. 天津大学学报(自然科学与工程技术版), 2008, 41(11): 1383-1390.
Wan Baikun, Zhou Zhongxing, Liu Yawei, et al.Detection of ERD/ERS Signals Based on ICA and PSD[J]. Journal of Tianjin University(Natural Science and Engineering Technology), 2008, 41(11): 1383-1390.
[10] Yang B H, Wu T, Wang Q, et al. Motor Imagery EEG Recognition Based on WPD-CSP and KF-SVM in Brain Computer Interfaces[J]. Applied Mechanics & Materials (S1662-7482), 2014, 556-562: 2829-2833.
[11] Li Y, Guan C, Li H, et al.A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system[J]. Pattern Recognition Letters (S0167-8655), 2008, 29(9): 1285-1294.
[12] Mehmood R M, Lee H J.Emotion classification of EEG brain signal using SVM and KNN[C]//. IEEE International Conference on Multimedia & Expo Workshops. IEEE, 2015: 1-5.
[13] 张宝利, 陈娟, 曹公正, 等. 从MATLAB版本间差异看VC与MATLAB混合编程趋势[J]. 长春工业大学学报(自然科学版), 2008, 29(2): 174-177.
Zhang Baoli, Chen Juan, Cao Zheng, et al.The mix program trend between VC and MATLAB in view of different MATLAB version[J]. Journal of Changchun University of Technology(Natural Science Edition), 2008, 29(2): 174-177.
[14] 王世香. 精通MATLAB接口与编程[M]. 北京: 电子工业出版社, 2007.
Wang Shixiang.Proficient in MATLAB interface and programming[M]. Beijing: Publishing House of Electronics Industry, 2007.
[15] Lee H H.Programming with MATLAB 2016[M]. Taiwan: SDC Publications, 2016.

基于脑机接口的康复训练系统

Rehabilitation Training System Based on Brain Computer Interface

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