基于脑电和眼电的运动想象多尺度识别方法研究

doi:10.3969/j.issn.0372-2112.2018.03.029

电子学报 ›› 2018, Vol. 46 ›› Issue (3): 714-720.DOI: 10.3969/j.issn.0372-2112.2018.03.029

基于脑电和眼电的运动想象多尺度识别方法研究

孙曜^1,2, 文成林², 韦巍¹

1. 浙江大学电气工程学院, 浙江杭州 310018;
2. 杭州电子科技大学自动化学院, 浙江杭州 310058

收稿日期:2016-12-13 修回日期:2017-01-24 出版日期:2018-03-25
- 作者简介:
- 孙曜,女,1975年生,安徽马鞍山人.浙江大学控制理论与控制工程专业博士研究生.主要研究方向为模式识别、生物医学信号处理.E-mail:sunyao@hdu.edu.cn;文成林,男,1963年生,河南开封人.1999年西北工业大学控制理论与控制工程专业毕业,获工学博士学位;2002年清华大学控制科学与工程博士后流动站出站.现为杭州电子科技大学教授,博士生导师,系统科学与控制工程研究所所长,中国电子学会高级会员,中国自动化学会过程故障诊断与技术专委会委员、中国自动化学会智能控制专委会委员,主要从事多源信息融合与目标检测识别和跟踪,复杂系统故障诊断与健康管理等领域的研究工作.E-mail:wencl@hdu.edu.cn;韦巍,男,1964年生,浙江东阳人.1994年浙江大学电气工程学院毕业,获工学博士学位.现为浙江大学教授、博士生导师,电气工程学院院长,中国电工技术学会理事、中国人工智能学会理事、IEEE Intelligent Systems Applications TC专委会委员,主要从事电气自动化、模式识别、智能系统与智能控制等方面的研究工作.E-mail:wwei@zju.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61503123，No.61304186）

Research on EEG and EOG Based Multiscale Recognization Method of Motor Imagery

SUN Yao^1,2, WEN Cheng-lin², WEI Wei¹

1. College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang 310018, China;
2. School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310058, China

Received:2016-12-13 Revised:2017-01-24 Online:2018-03-25 Published:2018-03-25
- Supported by:
- National Natural Science Foundation of China (No.61503123, No.61304186)

摘要/Abstract

摘要： 基于脑电信号对同一肢体不同动作想象模式进行识别的正确率低，已成为基于脑机接口对肢体瘫痪患者进行运动想象训练监控的方法，获得临床应用前必须解决的瓶颈问题.针对该问题，本文提出一种利用运动想象时眼睛的活动状态与所想象肢体动作之间存在的耦合关系，进行运动想象多尺度识别的新方法.该方法首先在大尺度上，利用脑电信号对运动想象是否发生进行识别，再结合同一运动想象过程眼电信号协同变化模式的识别结果，基于决策融合在更精细的尺度上，对同一肢体不同动作的想象模式进行识别.实验结果表明，仅基于脑电进行右臂三种动作想象模式识别的平均正确率为63.0%，而应用所提出方法可以将其提高到91.4%.所提出方法可望有临床应用前景.

关键词: 脑电, 眼电, 运动想象模式识别, 监控

Abstract: The accuracy on electroencephalogram (EEG) based motor imagery recognization of different movements of a same limb is low. It constrains the monitor method, which monitor motor imagery training of patients with limb paralysis based on brain computer interface, to be used in clinical. Aiming at the problem, in this paper a new method of multiscale recognization of motor imagery is proposed. It is based on the coupling relationship between eye movement and imagined movement during motor imagery. In this method, first of all EEG is applied to judge whether motor imagery occurs on a large scale, and then combined with the recognization result of electrooculogram (EOG), which changes cooperatively with EEG during the same motor imagery period, the different movements of a same limb is recognized using decision fusion on a finer scale. The results of experiments reveal that the average recognization accuracy of three kinds of movement imagery of the right arm can be promoted from 63. 0% (only with EEG) to 91. 4% (with the proposed method). The proposed method may be applied in clinical in the future.

Key words: electroencephalogram, electrooculogram, recognization of motor imagery, monitor

中图分类号:

TP391

孙曜, 文成林, 韦巍. 基于脑电和眼电的运动想象多尺度识别方法研究[J]. 电子学报, 2018, 46(3): 714-720.

SUN Yao, WEN Cheng-lin, WEI Wei . Research on EEG and EOG Based Multiscale Recognization Method of Motor Imagery[J]. Acta Electronica Sinica, 2018, 46(3): 714-720.

参考文献

[1] ETHIER C,MILLER L E.Brain-controlled muscle simulation for the restoration of motor function[J].Neurobiology of Disease,2015,83(2015):180-190.
[2] ANG KK,GUAN C,PHUA K.Facilitating effects of transcranial direct current stimulation on motor imagery brain computer interface with robotic feedback for stroke rehabilitation[J].Archives of Physical Medicine and Rehabilitation,2015,96(3):S79-S87.
[3] ALONSOL-VALERDI LM,SALIDO-RUIZ RA,RAMIREZ-MENDOZA RA.Motor imagery based brain computer interfaces:An emerging technology to rehabilitate motor deficits[J].Neuropsychologia,2015,79(PartB):354-363.
[4] SUN Y,WEI W,LUO ZZ,GAN HT,et al.Improving motor imagery practice with synchronous action observation in stroke patients[J].Topics in Stroke Rehabilitation,2016,23(4):245-253.
[5] GIRIJESH P,PAWEL H,DAMIEN C,SUZANNE MD,JACQUELINE C.Using motor imagery based brain-computer interface for post-stroke rehabilitation[A].Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering Antalya[C].Antalya,Turkey:IEEE Computer Society,2009.258-262.
[6] TEO WP,CHEW E.Is motor-imagery brain-computer interface feasible in stroke rehabilitation?[J].The American Academy of Physical Medicine and Rehabilitation,2014,6(8):723-728.
[7] NEUPER C,WÖRTZ M,PFURTSCHELLER G.ERD/ERS patterns reflecting sensorimotor activation and deactivation[J].Progress in Brain Research,2006,159(1):211-221.
[8] TEODORO SE,GEMOT MP,CLEMENS B.Analysis of sensorimotor rhythms for the implementation of a brain switch for healthy subjects[J].Biomedical Signal Processing and Control,2010,5(1):15-20.
[9] ALLISON BZ,BRUNNER C,KAISER V,et al.Toward a hybridbrain-computer interface based on imagined movement and visual attention[J].Journal of Neural Engineering,2010,7(2):1-9.
[10] HEREMANS E,HELSEN WF,FEYS P.The eyes as a mirror of our thoughts:Quantification of motor imagery of goal-directed movements through eye movement registration[J].Behavioural Brain Research,2008,187(2):351-360.
[11] GUEUGNEAU N,CROGNIER L,PAPAXANTHIS C.The influence of eye movements on the temporal features of executed and imagined arm movements[J].Brain Research,2008,1187(2008):95-102.
[12] FLANAGAN JR,JOHANSSON RS.Action plans used in action observation[J].Nature,2003,424(6950):769-771.
[13] POIROUX E,CAVARO-MéNARD C,Leruez S,et al.What do eye gaze metrics tell us about motor imagery?[J].Plos One,2015,10(11):1-15.
[14] WU JF,ANG AMS,TSUI KM,et al.Efficient implementation and design of a new single-channel electrooculography-based human machine interface system[J].IEEE Transactions on Circuits and Systems Ⅱ:Express Briefs,2015,62(2):179-183.
[15] HE P,WILSON G,RUSSEL C.Removal of ocular artifacts from electroencephalogram by adaptive filtering[J].Medical & Biological Engineering & Computing,2004,42(3):407-412.
[16] 安高云,阮秋琦.一种基于小波分解及鲁棒估计ICA算法及其在人脸识别中的应用.电子学报,2006,34(10):1900-1905. AN GY,RUAN QQ.A novel ICA algorithm for face recognition based on wavelet decomposition and robust estimation[J].Acta Electronica Sinica,2006,34(10):1900-1905.(in Chinese)
[17] 张焕,乔晓艳,王飞.基于小波-共空间模式的多类想象运动诱发脑电特征提取[J].测试技术学报,2015,29(6):523-528. ZHANG H,QIAO XY,WANG F.The feature extraction for EEG signal evoked by multi classification motor imagery based on wavelet-common spatial pattern analysis[J].Journal of Test and Measurement Technology,2015,29(6):523-528.(in Chinese)
[18] 徐宝国,宋爱国,费树岷.在线脑机接口中脑电信号的特征提取与分类方法[J].电子学报,2011,39(5):1025-1030. XU BG,SONG AG,FEI SM.Feature extraction and classification of EEG in online brain-computer interface[J].Acta Electronica Sinica,2011,39(5):1025-1030.(in Chinese)
[19] WU T,YAN GZ,YANG BH,et al.EEG feature extraction based on wavelet packet decomposition for brain computer interface[J].Measurement,2008,41(6):618-625.
[20] JIANG J,ZHOU ZT,YIN E,et al.Hybrid brain-computer interface (BCI) based on the EEG and EOG signals[J].BioMedical Materials and Engineering,2014,24(6):2919-2925.

基于脑电和眼电的运动想象多尺度识别方法研究

Research on EEG and EOG Based Multiscale Recognization Method of Motor Imagery

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李明爱, 张圆圆. 基于连续小波变换和符号传递熵的脑功能网络构建方法[J]. 电子学报, 2022, 50(7): 1600-1608.
[2]	欧阳与点, 谢鲲, 谢高岗, 文吉刚. 面向大规模网络测量的数据恢复算法：基于关联学习的张量填充[J]. 电子学报, 2022, 50(7): 1653-1663.
[3]	罗会兰, 陈鸿坤. 基于深度学习的目标检测研究综述[J]. 电子学报, 2020, 48(6): 1230-1239.
[4]	罗映雪, 贾博, 裘旭益, 邓平煜, 任和, 吴奇. 基于Gamma深度信念网络的飞行员脑疲劳状态识别[J]. 电子学报, 2020, 48(6): 1062-1070.
[5]	张学军, 景鹏, 何涛, 孙知信. 基于变分模态分解的癫痫脑电信号分类方法[J]. 电子学报, 2020, 48(12): 2469-2475.
[6]	彭丝雨, 周到, 张家琦, 王宇, 高军峰. 基于互信息的脑网络及测谎研究[J]. 电子学报, 2019, 47(7): 1551-1556.
[7]	司慧芳, 谢天, 高军峰, 官金安, 向州州, 兰长有, 卿训华. 基于相位延迟指数的脑功能网络及测谎研究[J]. 电子学报, 2018, 46(7): 1742-1747.
[8]	陈冉, 王鹏, 高军峰, 顾凌云, 黄文涛, 晏丹丹, 余雨蝶, 丁志雄. 利用相锁值算法的脑电相同步测谎研究[J]. 电子学报, 2018, 46(6): 1289-1293.
[9]	曾霖, 卓汉逵, 李磊. 基于智能规划的工作流任务识别算法[J]. 电子学报, 2018, 46(4): 871-877.
[10]	高军峰, 司慧芳, 余彬, 顾凌云, 梁莹, 杨勇. 基于脑电样本熵的测谎分析[J]. 电子学报, 2017, 45(8): 1836-1841.
[11]	邓磊, 陈宝华, 赖伟良, 陈志祥, 周杰. 三维监控系统中基于三维重构的交互式标定[J]. 电子学报, 2017, 45(3): 527-533.
[12]	李俊, 谢维信, 李良群, 刘钧彬. 基于模糊空时线索的多目标在线跟踪算法[J]. 电子学报, 2017, 45(3): 513-519.
[13]	刘志勇, 孙金玮. 基于序列连通度的睡眠分期算法研究[J]. 电子学报, 2017, 45(1): 225-231.
[14]	曹明伟, 余烨. 基于多层背景模型的运动目标检测[J]. 电子学报, 2016, 44(9): 2126-2133.
[15]	顾凌云, 吕文志, 杨勇, 高军峰, 官金安, 周到. 基于PCANet和SVM的谎言测试研究[J]. 电子学报, 2016, 44(8): 1969-1973.