搜索

x

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

引用本文:
Citation:

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
PDF
导出引用
  • 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).
    [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

  • [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

  • [1] 李朝锋, 王振, 刘欣宇, 杨苏辉, 徐震, 樊超阳. 基于VMD-ICA的水下激光雷达抗散射信号处理方法研究. 物理学报, 2024, 0(0): 0-0. doi: 10.7498/aps.73.20231993
    [2] 景鹏, 张学军, 孙知信. 基于弹性变分模态分解的癫痫脑电信号分类方法. 物理学报, 2021, 70(1): 018702. doi: 10.7498/aps.70.20200904
    [3] 许子非, 缪维跑, 李春, 金江涛, 李蜀军. 流场非线性特征提取与混沌分析. 物理学报, 2020, 69(24): 249501. doi: 10.7498/aps.69.20200625
    [4] 许子非, 岳敏楠, 李春. 优化递归变分模态分解及其在非线性信号处理中的应用. 物理学报, 2019, 68(23): 238401. doi: 10.7498/aps.68.20191005
    [5] 刘备, 胡伟鹏, 邹孝, 丁亚军, 钱盛友. 基于变分模态分解与多尺度排列熵的生物组织变性识别. 物理学报, 2019, 68(2): 028702. doi: 10.7498/aps.68.20181772
    [6] 杜义浩, 齐文靖, 邹策, 张晋铭, 谢博多, 谢平. 基于变分模态分解-相干分析的肌间耦合特性. 物理学报, 2017, 66(6): 068701. doi: 10.7498/aps.66.068701
    [7] 郭家梁, 钟宁, 马小萌, 张明辉, 周海燕. 基于振幅-周期二维特征的脑电样本熵分析. 物理学报, 2016, 65(19): 190501. doi: 10.7498/aps.65.190501
    [8] 雷敏, 孟光, 张文明, Nilanjan Sarkar. 基于虚拟开车环境的自闭症儿童脑电样本熵. 物理学报, 2016, 65(10): 108701. doi: 10.7498/aps.65.108701
    [9] 谢平, 杨芳梅, 陈晓玲, 杜义浩, 吴晓光. 基于多尺度传递熵的脑肌电信号耦合分析. 物理学报, 2015, 64(24): 248702. doi: 10.7498/aps.64.248702
    [10] 唐洁. 基于集合经验模态分解的类星体光变周期及其混沌特性分析. 物理学报, 2014, 63(4): 049701. doi: 10.7498/aps.63.049701
    [11] 黄晓林, 霍铖宇, 司峻峰, 刘红星. 等概率符号化样本熵应用于脑电分析. 物理学报, 2014, 63(10): 100503. doi: 10.7498/aps.63.100503
    [12] 王莹, 侯凤贞, 戴加飞, 刘新峰, 李锦, 王俊. 改进的相对转移熵的癫痫脑电分析. 物理学报, 2014, 63(21): 218701. doi: 10.7498/aps.63.218701
    [13] 姚文坡, 刘铁兵, 戴加飞, 王俊. 脑电信号的多尺度排列熵分析. 物理学报, 2014, 63(7): 078704. doi: 10.7498/aps.63.078704
    [14] 薛春芳, 侯威, 赵俊虎, 王式功. 集合经验模态分解在区域降水变化多尺度分析及气候变化响应研究中的应用. 物理学报, 2013, 62(10): 109203. doi: 10.7498/aps.62.109203
    [15] 吴莎, 李锦, 张明丽, 王俊. 基于改进的符号转移熵的心脑电信号耦合研究. 物理学报, 2013, 62(23): 238701. doi: 10.7498/aps.62.238701
    [16] 张志森, 龚志强, 支蓉. 利用传递熵对Lorenz系统和Walker环流信息传输方向的分析. 物理学报, 2013, 62(12): 129203. doi: 10.7498/aps.62.129203
    [17] 裴利军, 邱本花. 模态分解法在非恒同耦合系统同步研究中的推广. 物理学报, 2010, 59(1): 164-170. doi: 10.7498/aps.59.164
    [18] 曹寅文, 宋慎义, 肖井华. 运动后人体心肺节律同步关系及信号的耦合方向. 物理学报, 2010, 59(7): 5163-5168. doi: 10.7498/aps.59.5163
    [19] 何 亮, 杜 磊, 庄奕琪, 李伟华, 陈建平. 金属互连电迁移噪声的多尺度熵复杂度分析. 物理学报, 2008, 57(10): 6545-6550. doi: 10.7498/aps.57.6545
    [20] 支 蓉, 廉 毅, 封国林. 基于幂律尾指数研究不同尺度系统对降水的影响. 物理学报, 2007, 56(3): 1837-1842. doi: 10.7498/aps.56.1837
计量
  • 文章访问数:  7944
  • PDF下载量:  421
  • 被引次数: 0
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

/

返回文章
返回