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基于变分模态分解-传递熵的脑肌电信号耦合分析

谢平 杨芳梅 李欣欣 杨勇 陈晓玲 张利泰

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基于变分模态分解-传递熵的脑肌电信号耦合分析

谢平, 杨芳梅, 李欣欣, 杨勇, 陈晓玲, 张利泰

Functional coupling analyses of electroencephalogram and electromyogram based on variational mode decomposition-transfer entropy

Xie Ping, Yang Fang-Mei, Li Xin-Xin, Yang Yong, Chen Xiao-Ling, Zhang Li-Tai
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  • 皮层肌肉功能耦合是大脑皮层和肌肉组织间的相互作用, 脑肌电信号的多尺度耦合特征可以体现皮层-肌肉间多时空的功能联系. 本文引入变分模态分解并与传递熵结合, 构建变分模态分解-传递熵模型应用于脑肌间耦合研究. 首先基于变分模态分解将同步采集的脑电(EEG) 和肌电(EMG) 信号分别进行时频尺度化, 然后计算不同时频尺度间的传递熵值, 获取不同耦合方向(EEGEMG 及EMGEEG) 上不同尺度间的非线性耦合特征. 结果表明, 在静态握力输出条件下, 皮层与肌肉beta (1535 Hz) 频段间的耦合强度最为显著; EEGEMG 方向上脑电与肌电高gamma (5072 Hz) 频段的耦合强度总体上高于EMGEEG 方向.研究结果揭示皮层-肌肉功能耦合具有双向性, 且脑肌间不同耦合方向上、不同频段间的耦合强度有所差异.因此可利用变分模态分解-传递熵方法定量刻画大脑皮层与肌肉各时频段之间的非线性同步特征及功能联系.
    The functional corticomuscular coupling (FCMC) is defined as the interaction, coherence and time synchronism between cerebral cortex and muscle tissue, which could be revealed by the synchronization analyses of electroencephalogram (EEG) and electromyogram (EMG) firing in a target muscle. The FCMC analysis is an effective method to describe the information transfer and interaction in neuromuscular pathways. Forthermore, the multiscaled coherence analyses of EEG and EMG signals recorded simultaneously could describe the multiple spatial and temporal functional connection characteristics of FCMC, which could be helpful for understanding the multiple spatial and temporal coupling mechanism of neuromuscular system. In this paper, based on the adaptively decomposing signal into frequency band characteristis of variational mode decomposition (VMD) and the quantitatively detecting the directed exchange of information between two systems of transfer entropy (TE), a new methodvariational mode decomposition-transfer entropy (VMD-TE) is proposed. The VMD-TE method could quantitatively analyze the nonlinear functional connection characteristic on multiple time-frequency scales between EEG over brain scalp and surface EMG signals from flexor digitorum surerficialis, which are recorded simultaneously during grip task with steady-state force output.In this paper, application of VMD-TE method consists of two steps. Firstly, the EEG and EMG signals are adaptively decomposed into multi intrinsic mode functions based on variational mode decomposition method, respectively, to describe the information on different time-frequency scales. Then the transfer entropies between the different timefrequency scales of EEG and EMG are calculated to describe the nonlinear corticomuscular coupling characteristic in different pathways (EEGEMG and EMGEEG), to show the functional coupling strength (namely VMD-TE values). finally, the maximum VMD-TE values between the different time-frequency scales of EEG and EMG signals among the eight subjects are selected, to describe the discrepancies of FCMC interaction strength between all time-frequency scales. The results show that functional corticomuscular coupling is significant in both descending (EEGEMG) and ascending (EMGEEG) directions in the beta-band (15-35 Hz) in the static force output stage. Meanwhile, the interaction strength between EEG signal and the gamma band (50-72 Hz) of EMG signal in descending direction is higher than in ascending direction. Our study confirms that the beta oscillations of EEG travel bidirectionally between sensorimotor
      通信作者: 谢平, pingx@ysu.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 61271142)和河北省自然科学基金(批准号: F2015203372, F2014203246)资助的课题.
      Corresponding author: Xie Ping, pingx@ysu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61271142), and the Natural Science Foundation of Hebei Province, China (Grant Nos. F2015203372, F2014203246).
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    Witham C L, Riddle C N, Baker M R, Baker S N 2011 J. Physiol. 589 3789

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    Schelter B, Timmer J, Eichler M 2009 J. Neurosci. Meth. 179 121

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    Laine C M, Negro F, Farina D 2013 J. Neurophysiol. 110 170

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    Kristeva R, Patino L, Omlor W 2007 NeuroImage 36 785

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  • [1]

    Costa M, Goldberger A L, Peng C K 2002 Phys. Rev. Lett. 89 068102

    [2]

    Hu M, Liang H 2012 IEEE Trans. Bio-Med. Eng. 59 12

    [3]

    Thuraisingham R A, Gottwald G A 2006 Physica A 366 323

    [4]

    Zhang X, Chen X, Barkhaus P E, Zhou P {2013 IEEE Trans. Inf. Technol. B 17 470

    [5]

    Wu S D, Wu C W, Lee K Y, Lin S G 2013 Physica A 392 5865

    [6]

    Stamoulis C, Chang B S 2011 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30-September 3, 2011 p5908

    [7]

    Martis R J, Acharya U R, Tan J H, Petznick A, Ng E Y K, Tong L 2012 Int. J. Neural. Syst. 22 1250027

    [8]

    Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D 2013 35th Annual International Conference of the IEEE EMBS Osaka, Japan, July 3-7, 2013 p5754

    [9]

    Zhang X Q, Liang J 2013 Acta Phys. Sin. 62 050505 (in Chinese) [张学清, 梁军 2013 物理学报 62 050505]

    [10]

    Wu Z, Huang N E 2009 Advances in Adaptive Data Analysis 1 1

    [11]

    Chen D, Li D, Xiong M Z, Bao H, Li X L 2010 IEEE Trans. Inf. Technol. B 14 1417

    [12]

    Dragomiretskiy K, Zosso D {2014 IEEE Trans. Signal Proces. 62 531

    [13]

    Xie P, Yang F M, Chen X L, Du Y H, Wu X G 2015 Acta Phys. Sin. 64 248702 (in Chinese) [谢平, 杨芳梅, 陈晓玲, 杜义浩, 吴晓光 2015 物理学报 64 248702]

    [14]

    Yang Y F, Wu Y F, Ren X M, Qin W M, Zhi X Z, Qiu Y 2009 Acta Phys. Sin. 58 3746 (in Chinese) [杨永锋, 吴亚锋, 任兴民, 秦卫阳, 支希哲, 裘焱 2009 物理学报 58 3746]

    [15]

    Witham C L, Riddle C N, Baker M R, Baker S N 2011 J. Physiol. 589 3789

    [16]

    Schelter B, Timmer J, Eichler M 2009 J. Neurosci. Meth. 179 121

    [17]

    Laine C M, Negro F, Farina D 2013 J. Neurophysiol. 110 170

    [18]

    Androulidakis A G, Doyle L M, Yarrow K, Litvak V, Gilbertson T P, Brown P 2007 Eur. J. Neurosci. 25 3758

    [19]

    Kristeva R, Patino L, Omlor W 2007 NeuroImage 36 785

    [20]

    Gilbertson T, Lalo E, Doyle L, Di Lazzaro V, Cioni B, Brown P 2005 J. Neurosci. 25 7771

    [21]

    Androulidakis A G, Doyle L M, Gilbertson T P, Brown P 2006 Eur. J. Neurosci. 24 3299

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出版历程
  • 收稿日期:  2016-01-26
  • 修回日期:  2016-03-02
  • 刊出日期:  2016-06-05

基于变分模态分解-传递熵的脑肌电信号耦合分析

  • 1. 燕山大学电气工程学院河北省测试计量技术及仪器重点实验室, 秦皇岛 066004;
  • 2. 中国人民解放军北京军区第281医院 康复医学科, 秦皇岛 066100
  • 通信作者: 谢平, pingx@ysu.edu.cn
    基金项目: 国家自然科学基金(批准号: 61271142)和河北省自然科学基金(批准号: F2015203372, F2014203246)资助的课题.

摘要: 皮层肌肉功能耦合是大脑皮层和肌肉组织间的相互作用, 脑肌电信号的多尺度耦合特征可以体现皮层-肌肉间多时空的功能联系. 本文引入变分模态分解并与传递熵结合, 构建变分模态分解-传递熵模型应用于脑肌间耦合研究. 首先基于变分模态分解将同步采集的脑电(EEG) 和肌电(EMG) 信号分别进行时频尺度化, 然后计算不同时频尺度间的传递熵值, 获取不同耦合方向(EEGEMG 及EMGEEG) 上不同尺度间的非线性耦合特征. 结果表明, 在静态握力输出条件下, 皮层与肌肉beta (1535 Hz) 频段间的耦合强度最为显著; EEGEMG 方向上脑电与肌电高gamma (5072 Hz) 频段的耦合强度总体上高于EMGEEG 方向.研究结果揭示皮层-肌肉功能耦合具有双向性, 且脑肌间不同耦合方向上、不同频段间的耦合强度有所差异.因此可利用变分模态分解-传递熵方法定量刻画大脑皮层与肌肉各时频段之间的非线性同步特征及功能联系.

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