Intermuscular coupling based on wavelet packet-cross frequency coherence_Journal of Biomedical Engineering

Authors：

DU Yihao , BAI Xiaolin , YANG Wenjuan , ZHENG Lin ,  XIE Ping

Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;

Corresponding author：

XIE Ping, Email: pingx@ysu.edu.cn

Keywords：

electromyography signal; wavelet packet decomposition; n:m coherence analysis; nonlinear coupling

DOI：

10.7507/1001-5515.201908048

Video：

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Abstract Full text Figures/Tables Video References Cited by

Human motion control system has a high degree of nonlinear characteristics. Through quantitative evaluation of the nonlinear coupling strength between surface electromyogram (sEMG) signals, we can get the functional state of the muscles related to the movement, and then explore the mechanism of human motion control. In this paper, wavelet packet decomposition and n:m coherence analysis are combined to construct an intermuscular cross-frequency coupling analysis model based on wavelet packet-n:m coherence. In the elbow flexion and extension state with 30% maximum voluntary contraction force (MVC), sEMG signals of 20 healthy adults were collected. Firstly, the subband components were obtained based on wavelet packet decomposition, and then the n:m coherence of subband signals was calculated to analyze the coupling characteristics between muscles. The results show that the linear coupling strength (frequency ratio 1:1) of the cooperative and antagonistic pairs is higher than that of the nonlinear coupling (frequency ratio 1:2, 2:1 and 1:3, 3:1) under the elbow flexion motion of 30% MVC; the coupling strength decreases with the increase of frequency ratio for the intermuscular nonlinear coupling, and there is no significant difference between the frequency ratio n:m and m:n. The intermuscular coupling in beta and gamma bands is mainly reflected in the linear coupling (1:1), nonlinear coupling of low frequency ratio (1:2, 2:1) between synergetic pair and the linear coupling between antagonistic pairs. The results show that the wavelet packet-n:m coherence method can qualitatively describe the nonlinear coupling strength between muscles, which provides a theoretical reference for further revealing the mechanism of human motion control and the rehabilitation evaluation of patients with motor dysfunction.

Citation： DU Yihao, BAI Xiaolin, YANG Wenjuan, ZHENG Lin, XIE Ping. Intermuscular coupling based on wavelet packet-cross frequency coherence. Journal of Biomedical Engineering, 2020, 37(2): 288-295. doi: 10.7507/1001-5515.201908048 Copy

1.	Summers J J, Kagerer F A, Garry M I, et al. Bilateral and unilateral movement training on upper limb function in chronic stroke patients: A TMS study. J Neurol Sci, 2007, 252(1): 76-82.
2.	Baker S N. Oscillatory interactions between sensorimotor cortex and the periphery. Curr Opin Neurobiol, 2007, 17(6): 649-655.
3.	Walker S, Avela J, Wikgren J, et al. Aging and strength training influence knee extensor intermuscular coherence during low- and high-force isometric contractions. Front Physiol, 2018, 9: 1933.
4.	Kim J H. The effects of training using EMG biofeedback on stroke patients upper extremity functions. Journal of Physical Therapy Science, 2017, 29(6): 1085-1088.
5.	Mohr M, Schön T, von Tscharner V, et al. Intermuscular coherence between surface EMG signals is higher for monopolar compared to bipolar electrode configurations. Front Physiol, 2018, 9: 566.
6.	Laine C M, Valero-Cuevas F J. Intermuscular coherence reflects functional coordination. J Neurophysiol, 2017, 118(3): 1775-1783.
7.	Pereda E, Quiroga R Q, Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol, 2005, 77(1-2): 1-37.
8.	Chen Chunchuan, Kilner J M, Friston K J, et al. Nonlinear coupling in the human motor system. J Neurosci, 2010, 30(25): 8393-8399.
9.	Roberts J A, Robinson P A. Quantitative theory of driven nonlinear brain dynamics. Neuroimage, 2012, 62(3): 1947-1955.
10.	Yang Yuan, Solis-Escalante T, van der Helm F C, et al. A generalized coherence framework for detecting and characterizing nonlinear interactions in the nervous system. IEEE Trans Biomed Eng, 2016, 63(12): 2629-2637.
11.	Brown P, Farmer S F, Halliday D M, et al. Coherent cortical and muscle discharge in cortical myoclonus. Brain, 1999, 122(3): 461-472.
12.	Farmer S F, Gibbs J, Halliday D M, et al. Changes in EMG coherence between long and short thumb abductor muscles during human development. J Physiol, 2007, 579(2): 389-402.
13.	谢平, 宋妍, 郭子晖, 等. 中风康复运动中肌肉异常耦合分析. 生物医学工程学杂志, 2016, 33(2): 244-254.
14.	Jin S H, Lin P, Hallett M. Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction. Clin Neurophysiol, 2010, 121(3): 392-401.
15.	Diab A, Hassan M, Boudaoud S, et al. Nonlinear estimation of coupling and directionality between signals: application to uterine EMG propagation//35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013: 4366-4369.
16.	Rojas-Martínez M, Alonso J F, Chaler J, et al. Analysis of muscle coupling during isokinetic endurance contractions by means of nonlinear prediction//35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013: 5005-5008.
17.	Yang Yuan, Solis-Escalante T, Yao Jun, et al. Nonlinear connectivity in the human stretch reflex assessed by cross-frequency phase coupling. Int J Neural Syst, 2016, 26(8): 1650043.
18.	Yang Yuan, Solis-Escalante T, van de Ruit M, et al. Nonlinear coupling between cortical oscillations and muscle activity during isotonic wrist flexion. Front Comput Neurosci, 2016, 10: 126.
19.	Aru J, Aru J, Priesemann V, et al. Untangling cross-frequency coupling in neuroscience. Curr Opin Neurobiol, 2015, 31: 51-61.
20.	杜义浩, 齐文靖, 邹策, 等. 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): 345-354.
21.	Grosse P, Cassidy M J, Brown P. EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: physiological principles and clinical applications. Clinical Neurophysiology, 2002, 113(10): 1523-1531.
22.	Reyes A, Laine C M, Kutch J J, et al. Beta band corticomuscular drive reflects muscle coordination strategies. Front Comput Neurosci, 2017, 11: 17.
23.	Omlor W, Patino L, Hepp-Reymond M C, et al. Gamma-range corticomuscular coherence during dynamic force output. Neuroimage, 2007, 34(3): 1191-1198.
24.	Gwin J T, Ferris D P. Beta- and gamma-range human lower limb corticomuscular coherence. Front Hum Neurosci, 2012, 6: 258.
25.	Hyafil A, Giraud A L, Fontolan L A. Neural Cross-Frequency coupling: connecting architectures, mechanisms, and functions. Trends Neurosci, 2015, 38(11): 725-740.

1. Summers J J, Kagerer F A, Garry M I, et al. Bilateral and unilateral movement training on upper limb function in chronic stroke patients: A TMS study. J Neurol Sci, 2007, 252(1): 76-82.
2. Baker S N. Oscillatory interactions between sensorimotor cortex and the periphery. Curr Opin Neurobiol, 2007, 17(6): 649-655.
3. Walker S, Avela J, Wikgren J, et al. Aging and strength training influence knee extensor intermuscular coherence during low- and high-force isometric contractions. Front Physiol, 2018, 9: 1933.
4. Kim J H. The effects of training using EMG biofeedback on stroke patients upper extremity functions. Journal of Physical Therapy Science, 2017, 29(6): 1085-1088.
5. Mohr M, Schön T, von Tscharner V, et al. Intermuscular coherence between surface EMG signals is higher for monopolar compared to bipolar electrode configurations. Front Physiol, 2018, 9: 566.
6. Laine C M, Valero-Cuevas F J. Intermuscular coherence reflects functional coordination. J Neurophysiol, 2017, 118(3): 1775-1783.
7. Pereda E, Quiroga R Q, Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol, 2005, 77(1-2): 1-37.
8. Chen Chunchuan, Kilner J M, Friston K J, et al. Nonlinear coupling in the human motor system. J Neurosci, 2010, 30(25): 8393-8399.
9. Roberts J A, Robinson P A. Quantitative theory of driven nonlinear brain dynamics. Neuroimage, 2012, 62(3): 1947-1955.
10. Yang Yuan, Solis-Escalante T, van der Helm F C, et al. A generalized coherence framework for detecting and characterizing nonlinear interactions in the nervous system. IEEE Trans Biomed Eng, 2016, 63(12): 2629-2637.
11. Brown P, Farmer S F, Halliday D M, et al. Coherent cortical and muscle discharge in cortical myoclonus. Brain, 1999, 122(3): 461-472.
12. Farmer S F, Gibbs J, Halliday D M, et al. Changes in EMG coherence between long and short thumb abductor muscles during human development. J Physiol, 2007, 579(2): 389-402.
13. 谢平, 宋妍, 郭子晖, 等. 中风康复运动中肌肉异常耦合分析. 生物医学工程学杂志, 2016, 33(2): 244-254.
14. Jin S H, Lin P, Hallett M. Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction. Clin Neurophysiol, 2010, 121(3): 392-401.
15. Diab A, Hassan M, Boudaoud S, et al. Nonlinear estimation of coupling and directionality between signals: application to uterine EMG propagation//35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013: 4366-4369.
16. Rojas-Martínez M, Alonso J F, Chaler J, et al. Analysis of muscle coupling during isokinetic endurance contractions by means of nonlinear prediction//35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013: 5005-5008.
17. Yang Yuan, Solis-Escalante T, Yao Jun, et al. Nonlinear connectivity in the human stretch reflex assessed by cross-frequency phase coupling. Int J Neural Syst, 2016, 26(8): 1650043.
18. Yang Yuan, Solis-Escalante T, van de Ruit M, et al. Nonlinear coupling between cortical oscillations and muscle activity during isotonic wrist flexion. Front Comput Neurosci, 2016, 10: 126.
19. Aru J, Aru J, Priesemann V, et al. Untangling cross-frequency coupling in neuroscience. Curr Opin Neurobiol, 2015, 31: 51-61.
20. 杜义浩, 齐文靖, 邹策, 等. 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): 345-354.
21. Grosse P, Cassidy M J, Brown P. EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: physiological principles and clinical applications. Clinical Neurophysiology, 2002, 113(10): 1523-1531.
22. Reyes A, Laine C M, Kutch J J, et al. Beta band corticomuscular drive reflects muscle coordination strategies. Front Comput Neurosci, 2017, 11: 17.
23. Omlor W, Patino L, Hepp-Reymond M C, et al. Gamma-range corticomuscular coherence during dynamic force output. Neuroimage, 2007, 34(3): 1191-1198.
24. Gwin J T, Ferris D P. Beta- and gamma-range human lower limb corticomuscular coherence. Front Hum Neurosci, 2012, 6: 258.
25. Hyafil A, Giraud A L, Fontolan L A. Neural Cross-Frequency coupling: connecting architectures, mechanisms, and functions. Trends Neurosci, 2015, 38(11): 725-740.

Journal of Biomedical Engineering

Intermuscular coupling based on wavelet packet-cross frequency coherence

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