Search

Article

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

Functional coupling analyses of electroencephalogram and electromyogram based on multiscale transfer entropy

Xie Ping Yang Fang-Mei Chen Xiao-Ling Du Yi-Hao Wu Xiao-Guang

Citation:

Functional coupling analyses of electroencephalogram and electromyogram based on multiscale transfer entropy

Xie Ping, Yang Fang-Mei, Chen Xiao-Ling, Du Yi-Hao, Wu Xiao-Guang
PDF
Get Citation

(PLEASE TRANSLATE TO ENGLISH

BY GOOGLE TRANSLATE IF NEEDED.)

  • Synchronization analyses of electroencephalogram (EEG) and electromyogram (EMG) could reveal the functional corticomuscular coupling (FCMC) between sensorimotor cortex and motor units firing in a target muscle. In order to quantitatively analyze the nonlinear functional coupling characteristics of EEG and EMG on a multiple time scale, a multiscale transfer entropy (MSTE) method based on the transfer entropy theory is proposed. Considering the multi-scale characteristics of EEG and EMG signals, the EEG and EMG signals are firstly decomposed into multiscale ones, respectively, to show the information on different time scales. Then the signals on different time scales are decomposed into different frequency bands to show the frequency domain characteristics. Finally, the EEG and EMG in different frequency bands on different scales are calculated by the MSTE method to obtain the FCMC characteristics on different time scales and in coupling frequency bands. In this study the MSTE is used to quantitatively analyze the nonlinear functional connection between EEG over the brain scalp and the surface EMG from the flexor digitorum surerficialis (FDS), which are recorded simultaneously during grip task with steady-state force output.#br#In the process of data processing, the coarse graining method is introduced firstly to decompose the EEG and EMG recorded in the task. Secondly, MSTEs between EEG and EMG on various scales are calculated to describe the nonlinear FCMC characteristics in different pathways (EEG→EMG and EMG→EEG). Furthermore, a significant indicator of MSTE is defined to quantitatively analyze the discrepancy between FCMC interaction strengths in the specific frequency band. The results show that the functional corticomuscular coupling is significant in both descending (EEG→EMG) and ascending (EMG→EEG) directions in the beta-band (15-35 Hz) in the static force output stage, especially that the interaction strength in descending direction is stronger in beta2-band (15-35 Hz) than that in the ascending direction. Meanwhile, the maximum FCMC strength value and the maximum or minimum discrepancy value between coupling directions on different scales almost occur on the high scales (15-30). Our study confirms that beta oscillations of EEG travel bidirectionally between the sensorimotor cortex and contralateral muscles in the sensorimotor loop system, and beta2 band is likely to reflect the motor control commands from the cortex to the muscle. Additionally, the discrepancy varies on different time scales and in different coupling frequency bands. The results show that the MSTE can quantitatively estimate the nonlinear interconnection and functional corticomuscular coupling between the sensorimotor cortex and the muscle.
      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]

    Chiang J, Wang Z J, McKeown M J 2012 NeuroImage 63 1498

    [2]

    Conway B A, Halliday D M, Shahani U, Maas P, Weir A I, Rosenberg J R, Farmer S F 1995 J. Physiol. 483 35

    [3]

    Johnson A N, Shinohara M 2012 Eur. J. Appl. Physiol. 112 970

    [4]

    Omlor W, Patino L, Hepp-Reymond M C, Kristeva R 2007 NeuroImage 34 1191

    [5]

    Slobounov S, Ray W, Cao C, Chiang H 2007 Neurosci. Lett. 421 126

    [6]

    Mima T, Ohara S, Nagamine T 2002 Cortical-Muscular Coherence Int. Congr. Ser. (Vol. 1226) (Netherlands: Elsevier) pp109-119

    [7]

    Seth A K 2010 J. Neurosci. Meth. 186 262

    [8]

    Sitnikova E, Dikanev T, Smirnov D, Bezruchko B, Van Luijtelaar G 2008 J. Neurosci. Meth. 170 245

    [9]

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

    [10]

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

    [11]

    Schreiber T 2000 Phys. Rev. Lett. 85 461

    [12]

    Wu S, Li J, Zhang M L, Wang J 2013 Acta Phys. Sin. 62 238701 (in Chinese) [吴莎, 李锦, 张明丽, 王俊 2013 62 238701]

    [13]

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

    [14]

    Yao W P, Liu T B, Dai J F, Wang J 2014 Acta Phys. Sin. 63 078704 (in Chinese) [姚文坡, 刘铁兵, 戴加飞, 王俊 2014 63 078704]

    [15]

    Yan B G, Zhao T T 2011 Acta Phys. Sin. 60 078701 (in Chinese) [严碧歌, 赵婷婷 2011 60 078701]

    [16]

    Costa M, Goldberger A L, Peng C K 2005 Phys. Rev. E 71 021906

    [17]

    Ma P P, Chen Y Y, Du Y H, Su Y P, Wu X G, Liang Z H, Xie P 2014 Journal of Biomedical Engineering 31 971 (in Chinese) [马培培, 陈迎亚, 杜义浩, 苏玉萍, 吴晓光, 梁振虎, 谢平 2014 生物医学工程学杂志 31 971]

    [18]

    Vecchio F, Del Percio C, Marzano N, Fiore A, Toran G, Aschieri P, Gallamini M, Cabras J, Rossini P M, Babiloni, Eusebi F 2008 Behav. Neurosci. 122 917

    [19]

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

    [20]

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

    [21]

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

    [22]

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

    [23]

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

    [24]

    Mima T, Matsuoka T, Hallett M 2001 Clin. Neurophy-siol. 112 122

  • [1]

    Chiang J, Wang Z J, McKeown M J 2012 NeuroImage 63 1498

    [2]

    Conway B A, Halliday D M, Shahani U, Maas P, Weir A I, Rosenberg J R, Farmer S F 1995 J. Physiol. 483 35

    [3]

    Johnson A N, Shinohara M 2012 Eur. J. Appl. Physiol. 112 970

    [4]

    Omlor W, Patino L, Hepp-Reymond M C, Kristeva R 2007 NeuroImage 34 1191

    [5]

    Slobounov S, Ray W, Cao C, Chiang H 2007 Neurosci. Lett. 421 126

    [6]

    Mima T, Ohara S, Nagamine T 2002 Cortical-Muscular Coherence Int. Congr. Ser. (Vol. 1226) (Netherlands: Elsevier) pp109-119

    [7]

    Seth A K 2010 J. Neurosci. Meth. 186 262

    [8]

    Sitnikova E, Dikanev T, Smirnov D, Bezruchko B, Van Luijtelaar G 2008 J. Neurosci. Meth. 170 245

    [9]

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

    [10]

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

    [11]

    Schreiber T 2000 Phys. Rev. Lett. 85 461

    [12]

    Wu S, Li J, Zhang M L, Wang J 2013 Acta Phys. Sin. 62 238701 (in Chinese) [吴莎, 李锦, 张明丽, 王俊 2013 62 238701]

    [13]

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

    [14]

    Yao W P, Liu T B, Dai J F, Wang J 2014 Acta Phys. Sin. 63 078704 (in Chinese) [姚文坡, 刘铁兵, 戴加飞, 王俊 2014 63 078704]

    [15]

    Yan B G, Zhao T T 2011 Acta Phys. Sin. 60 078701 (in Chinese) [严碧歌, 赵婷婷 2011 60 078701]

    [16]

    Costa M, Goldberger A L, Peng C K 2005 Phys. Rev. E 71 021906

    [17]

    Ma P P, Chen Y Y, Du Y H, Su Y P, Wu X G, Liang Z H, Xie P 2014 Journal of Biomedical Engineering 31 971 (in Chinese) [马培培, 陈迎亚, 杜义浩, 苏玉萍, 吴晓光, 梁振虎, 谢平 2014 生物医学工程学杂志 31 971]

    [18]

    Vecchio F, Del Percio C, Marzano N, Fiore A, Toran G, Aschieri P, Gallamini M, Cabras J, Rossini P M, Babiloni, Eusebi F 2008 Behav. Neurosci. 122 917

    [19]

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

    [20]

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

    [21]

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

    [22]

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

    [23]

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

    [24]

    Mima T, Matsuoka T, Hallett M 2001 Clin. Neurophy-siol. 112 122

  • [1] Jing Peng, Zhang Xue-Jun, Sun Zhi-Xin. eEpileptic electroencephalogram signal classification method based on elastic variational mode decomposition. Acta Physica Sinica, 2021, 70(1): 018702. doi: 10.7498/aps.70.20200904
    [2] Du Yi-Hao, Qi Wen-Jing, Zou Ce, Zhang Jin-Ming, Xie Bo-Duo, Xie Ping. Intermuscular coupling characteristics based on variational mode decomposition-coherence. Acta Physica Sinica, 2017, 66(6): 068701. doi: 10.7498/aps.66.068701
    [3] Xie Ping, Yang Fang-Mei, Li Xin-Xin, Yang Yong, Chen Xiao-Ling, Zhang Li-Tai. Functional coupling analyses of electroencephalogram and electromyogram based on variational mode decomposition-transfer entropy. Acta Physica Sinica, 2016, 65(11): 118701. doi: 10.7498/aps.65.118701
    [4] Lei Min, Meng Guang, Zhang Wen-Ming, Nilanjan Sarkar. Sample entropy of electroencephalogram for children with autism based on virtual driving game. Acta Physica Sinica, 2016, 65(10): 108701. doi: 10.7498/aps.65.108701
    [5] Zhang Tao, Chen Wan-Zhong, Li Ming-Yang. Recognition of epilepsy electroencephalography based on AdaBoost algorithm. Acta Physica Sinica, 2015, 64(12): 128701. doi: 10.7498/aps.64.128701
    [6] Wang Ying, Hou Feng-Zhen, Dai Jia-Fei, Liu Xin-Feng, Li Jin, Wang Jun. Transfer entropy analysis of electroencephalogram based on adaptive template method. Acta Physica Sinica, 2015, 64(8): 088701. doi: 10.7498/aps.64.088701
    [7] Yang Jian, Chen Shu-Shen, Huangfu Hao-Ran, Liang Pei-Peng, Zhong Ning. Dynamic functional connectivity of electroencephalogram in the resting state. Acta Physica Sinica, 2015, 64(5): 058701. doi: 10.7498/aps.64.058701
    [8] Yao Wen-Po, Liu Tie-Bing, Dai Jia-Fei, Wang Jun. Multiscale permutation entropy analysis of electroencephalogram. Acta Physica Sinica, 2014, 63(7): 078704. doi: 10.7498/aps.63.078704
    [9] Wang Ying, Hou Feng-Zhen, Dai Jia-Fei, Liu Xin-Feng, Li Jin, Wang Jun. Analysis on relative transfer of entropy based on improved epileptic EEG. Acta Physica Sinica, 2014, 63(21): 218701. doi: 10.7498/aps.63.218701
    [10] Wang Kai-Ming, Zhong Ning, Zhou Hai-Yan. Activity analysis of depression electroencephalogram based on modified power spectral entropy. Acta Physica Sinica, 2014, 63(17): 178701. doi: 10.7498/aps.63.178701
    [11] Wu Yong-Feng, Huang Shao-Ping, Jin Guo-Bin. Study on partial discharge signal detection by coupled Duffing oscillators. Acta Physica Sinica, 2013, 62(13): 130505. doi: 10.7498/aps.62.130505
    [12] Zhang Mei, Cui Chao, Ma Qian-Li, Gan Zong-Liang, Wang Jun. Coupling analysis of multivariate bioelectricity signal based symbolic partial mutual information. Acta Physica Sinica, 2013, 62(6): 068704. doi: 10.7498/aps.62.068704
    [13] Wu Sha, Li Jin, Zhang Ming-Li, Wang Jun. Coupling analysis of electrocardiogram and electroencephalogram based on improved symbolic transfer entropy. Acta Physica Sinica, 2013, 62(23): 238701. doi: 10.7498/aps.62.238701
    [14] Zhang Mei, Wang Jun. Modified symbolic relative entropy based electroencephalogram time irreversibility analysis. Acta Physica Sinica, 2013, 62(3): 038701. doi: 10.7498/aps.62.038701
    [15] Yang Xiao-Niu, Li Jian-Dong, Tang Zhi-Ling. Study on fractal features of modulated radio signal. Acta Physica Sinica, 2011, 60(5): 056401. doi: 10.7498/aps.60.056401
    [16] Bian Hong-Rui, Wang Jiang, Han Chun-Xiao, Deng Bin, Wei Xi-Le, Che Yan-Qiu. Features extraction from EEG signals induced by acupuncture based on the complexity analysis. Acta Physica Sinica, 2011, 60(11): 118701. doi: 10.7498/aps.60.118701
    [17] Shen Wei, Wang Jun. Time irreversibility analysis of ECG based on symbolic relative entropy. Acta Physica Sinica, 2011, 60(11): 118702. doi: 10.7498/aps.60.118702
    [18] Ma Qian-Li, Bian Chun-Hua, Wang Jun. Scaling analysis on electroencephalogram and its application to sleep-staging. Acta Physica Sinica, 2010, 59(7): 4480-4484. doi: 10.7498/aps.59.4480
    [19] Meng Qing-Fang, Zhou Wei-Dong, Chen Yue-Hui, Peng Yu-Hua. The feature extraction of epileptic EEG signals based on nonlinear prediction. Acta Physica Sinica, 2010, 59(1): 123-130. doi: 10.7498/aps.59.123
    [20] You Rong-Yi, Chen Zhong, Xu Shen-Chu, Wu Bo-Xi. Study on phase-space reconstruction of chaotic signal based on wavelet transform. Acta Physica Sinica, 2004, 53(9): 2882-2888. doi: 10.7498/aps.53.2882
Metrics
  • Abstract views:  8629
  • PDF Downloads:  384
  • Cited By: 0
Publishing process
  • Received Date:  09 June 2015
  • Accepted Date:  03 July 2015
  • Published Online:  05 December 2015

/

返回文章
返回
Baidu
map