2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于运动相关皮层电位握力运动模式识别研究

伏云发 徐保磊 李永程 李洪谊 王越超 余正涛

伏云发, 徐保磊, 李永程, 李洪谊, 王越超, 余正涛. 基于运动相关皮层电位握力运动模式识别研究. 自动化学报, 2014, 40(6): 1045-1057. doi: 10.3724/SP.J.1004.2014.01045
引用本文: 伏云发, 徐保磊, 李永程, 李洪谊, 王越超, 余正涛. 基于运动相关皮层电位握力运动模式识别研究. 自动化学报, 2014, 40(6): 1045-1057. doi: 10.3724/SP.J.1004.2014.01045
FU Yun-Fa, XU Bao-Lei, LI Yong-Cheng, LI Hong-Yi, WANG Yue-Chao, YU Zheng-Tao. Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials. ACTA AUTOMATICA SINICA, 2014, 40(6): 1045-1057. doi: 10.3724/SP.J.1004.2014.01045
Citation: FU Yun-Fa, XU Bao-Lei, LI Yong-Cheng, LI Hong-Yi, WANG Yue-Chao, YU Zheng-Tao. Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials. ACTA AUTOMATICA SINICA, 2014, 40(6): 1045-1057. doi: 10.3724/SP.J.1004.2014.01045

基于运动相关皮层电位握力运动模式识别研究

doi: 10.3724/SP.J.1004.2014.01045
基金项目: 

国家自然科学基金青年基金(60705021),云南省应用基础研究计划项目(2013FB026),云南省级人培项目(KKSY201303048),云南省教育厅重点项目(2013Z130)资助

详细信息
    作者简介:

    李洪谊 中国科学院沈阳自动化研究所研究员. 主要研究方向为医疗机器人系统,机器人遥操作,微小机器人,机器人学与认知科学相结合的新型人机融合技术,非线性控制. E-mail:hli@sia.cn

Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials

Funds: 

Supported by National Natural Science Foundation of Youth Fund of China (60705021), Research Project for Application Foundation of Yunnan Province (2013FB026), Cultivation Program of Talents of Yunnan Province (KKSY201303048), and Focal Program for Education Office of Yunnan Province (2013Z130)

  • 摘要: 面向基于脑-机接口(Brain-computer interface,BCI)的脑-机交互控制(Brain-machine interaction control,BMIC)——直接脑控机器人,提出一种新的左、右手握力运动参数范式,在该范式下探索左、右手握力运动相关皮层电位/运动相关电位(Movement-related potentials,MRPs)的时域特征表示并识别握力运动模式.在涉及左、右手4个不同任务的实验中采集了11个健康被试的脑电信号,任务期间要求被试以2种握力变化模式之一完成自愿握力运动,每种任务随机重复30次.不同握力任务之间具有显著差异的运动相关电位特征用于识别握力运动模式.分别用基于核的Fisher线性判别分析和支持向量机识别4个不同的握力运动任务.研究结果进一步证实运动相关电位可以表征握力运动规划、运动执行和运动监控的脑神经机制过程.基于核的Fisher线性判别分析和支持向量机分别获得24±4%和21±5%的平均错误分类率.最小误分类率是12%,所有被试平均最小误分类率为20.9±5%.与传统的仅仅识别参与运动的肢体类型以及识别单侧肢体运动参数的研究相比,本研究可望为脑-机交互控制/脑控机器人接口提供更多的力控制意图指令,奠定了后续的对比研究基础.
  • [1] Scott S H. Converting thoughts into action. Nature, 2006, 442(7099): 141-142
    [2] Wolpaw J R, Birbaumer N, Heetderks W J, McFarland D J, Peckham P H, Schalk G, Donchin E, Quatrano L A, Robinson C J, Vaughan T M. Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2): 164-173
    [3] Wang Xing-Yu, Jin Jing, Zhang Yu, Wang Bei. Brain control: human-computer integration control based on brain-computer interface. Acta Automatica Sinica, 2013, 39(3): 208-221(王行愚, 金晶, 张宇, 王蓓. 脑控: 基于脑-机接口的人机融合控制. 自动化学报, 2013, 39(3): 208-221)
    [4] Fu Yun-Fa, Wang Yue-Chao, Li Hong-Yi, Xu Bao-Lei, Li Yong-Cheng. Direct brain-controlled robot interface technology. Acta Automatica Sinica, 2012, 38(8): 1229-1246(伏云发, 王越超, 李洪谊, 徐保磊, 李永程. 直接脑控机器人接口技术. 自动化学报, 2012, 38(8): 1229-1246)
    [5] Decety J. The neurophysiological basis of motor imagery. Behavioural Brain Research, 1996, 77(1-2): 45-52
    [6] Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 2001, 89(7): 1123-1134
    [7] Cunnington R, Iansek R, Bradshaw J L, Phillips J G. Movement-related potentials associated with movement preparation and motor imagery. Experimental Brain Research, 1996, 111(3): 429-436
    [8] Pfurtscheller G, Lopes da Silva F H. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 1999, 110(11): 1842-1857
    [9] Neuper C, Wörtz M, Pfurtscheller G. ERD/ERS patterns reflecting sensorimotor activation and deactivation. Progress in Brain Research, 2006, 159: 211-222
    [10] Shibasaki H, Hallett M. What is the bereitschaftspotential? Clinical Neurophysiology, 2006, 117(11): 2341-2356
    [11] do Nascimento O F, Nielsen K D, Voigt M. Movement related parameters modulate cortical activity during imaginary isometric plantar-flexions. Experimental Brain Research, 2006, 171(1): 78-90
    [12] Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M. EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology, 1997, 103(6): 642-651
    [13] Neuper C, Schlögl A, Pfurtscheller G. Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. Journal of Clinical Neurophysiology, 1999, 16(4): 373-382
    [14] Schlösgl A, Lee F, Bischof H, Pfurtscheller G. Characterization of four class motor imagery EEG data for the BCI-competition 2005. Journal of Neural Engineering, 2005, 2(4): L14-L22
    [15] Gu Y, Dremstrup K, Farina D. Single-trial discrimination of type and speed of wrist movements from EEG recordings. Clinical Neurophysiology, 2009, 120(8): 1596-1600
    [16] Gu Y, Farina D, Murguialday A R, Dremstrup K, Montoya P, Birbaumer N. Offline identification of imagined speed of wrist movements in paralyzed ALS patients from single-trial EEG. Frontiers in Neuroscience, 2009, 3: 62
    [17] Gu Y, do Nascimento O F, Lucase M F, Farina D. Identification of task parameters from movement-related cortical potentials. Medical Biological Engineering Computer, 2009, 47(12): 1257-1264
    [18] do Nascimento O F, Farina D. Movement-related cortical potentials allow discrimination of rate of torque development in imaginary isometric plantar flexion. IEEE Transactions on Biomedical Engineering, 2008, 55(11): 2675-2678
    [19] Farina D, do Nascimento O F, Lucas M F, Doncarli C. Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters. Journal of Neuroscience Methods, 20078, 162(1-2): 357-363
    [20] Fu Y F, Xu B L, Li Y C, Wang Y C, Li H Y, Yu Z T. The single-trial decoding of imagined grip force parameters involved left and right hands based on movement-related cortical potentials. Chinese Science Bulletin, 2014, 59(16): 1907-1916
    [21] Yuan H, Perdoni C, He B. Relationship between speed and EEG activity during imagined and executed hand movements. Journal of Neural Engineering, 2010, 7(2): 26001
    [22] Romero D H, Lacourse M G, Lawrencea M G, Schandlera S, Cohen M J. Event-related potentials as a function of movement parameter variations during motor imagery and isometric action. Behavioural Brain Research, 2000, 117(1-2): 83-96
    [23] Klem G H, Lüders H O, Jasper H H, Elger C E. The ten-twenty electrode system of the International Federation of Clinical Neurophysiology. Electroencephalography Clinical Neurophysiology, 1999, 52(S2): 3-6
    [24] Yang Shu-Ying. Pattern Recognition and Intelligent Computing: Matlab Technology Realization (2nd Edition). Beijing: Publishing House of Electronics Industry, 2011 (杨淑莹. 模式识别与智能计算——Matlab技术实现. 第2版. 北京: 电子工业出版社, 2011)
    [25] Slobounov S M, Ray W J. Movement-related potentials with reference to isometric force output in discrete and repetitive tasks. Experimental Brain Research, 1998, 123(4): 461-473
    [26] Shibasaki H, Barrett G, Halliday E, Halliday A M. Cortical potentials associated with voluntary foot movement in man. Electroencephalography and Clinical Neurophysiology, 1981, 52(6): 507-516
    [27] do Nascimento O F, Nielsen K D, Voigt M. Relationship between plantar-flexor torque generation and the magnitude of the movement-related potentials. Experimental Brain Research, 2005, 160(2): 154-165
  • 加载中
计量
  • 文章访问数:  2360
  • HTML全文浏览量:  60
  • PDF下载量:  1572
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-12-13
  • 修回日期:  2013-08-01
  • 刊出日期:  2014-06-20

目录

    /

    返回文章
    返回