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

doi:10.7498/aps.65.118701

Abstract

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

Keywords:

PACS:

87.85.Ng	(Biological signal processing)
05.45.-a	(Nonlinear dynamics and chaos)

Authors and contacts

1.
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China;
2.
Department of Rehabilitation Medicine, the No. 281 Hospital of Chinese People's Liberation Army, Qinhuangdao 066100, China

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).

References

[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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其他类型引用(40)

[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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期刊类型引用(35)

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. 物理学报. 2024(09): 91-96 . 百度学术
2.	郑杨，强威，张四聪. 脑-运动单元功能耦合估计方法. 西安交通大学学报. 2024(12): 111-118 . 百度学术
3.	刘斌. 基于CNN-LSTM多层次特征融合模型的癫痫发作预测研究. 电脑与电信. 2024(11): 66-73 . 百度学术
4.	翟雪松，许家奇，陈鑫源，楚肖燕，李雨珊，李媛. 文本呈现方式对视觉舒适度的影响——基于人工智能与脑电协同的分析. 开放教育研究. 2023(01): 70-80 . 百度学术
5.	沈友东，贺小星，张云涛. EMD与VMD组合站坐标时间序列降噪方法. 海洋测绘. 2023(01): 44-48 . 百度学术
6.	李佳佳，冯钊，赵亮. 运动态人脑默认模式网络脑电信息传递动力学行为分析. 西安交通大学学报. 2023(06): 18-28 . 百度学术
7.	金国美，佘青山，张敏，马玉良，张建海，孙明旭. 多尺度补偿传递熵的皮层肌肉功能耦合方法. 浙江大学学报(工学版). 2022(06): 1152-1158+1256 . 百度学术
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