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肌间耦合是肢体运动过程中不同肌肉间的相互关联与相互协调作用.通过研究肌电信号（sEMG）间特征频段的耦合特性可以获得肌肉间的功能联系及中枢神经系统支配肢体运动的执行与协调方式机理.本文将变分模态分解与相干分析相结合，构建变分模态分解-相干分析模型，定量描述肢体运动中相关肌肉sEMG在特征频段的耦合特性.在20%最大自主收缩力静态负荷强度下，采集20名健康被试的sEMG，基于变分模态分解方法将sEMG时频尺度化，进而分析不同sEMG在特征频段的相干性，并计算显著相干面积指标，定量分析肌间特征频段的功能耦合特性.结果表明：低负荷静态握力维持过程中，指浅屈肌与尺侧腕曲肌、指浅屈肌与指伸肌的beta与gamma频段耦合强度随时间推进而增强；相较于指浅屈肌与指伸肌，疲劳状态下指浅屈肌与尺侧腕曲肌beta与gamma频段耦合强度变化更显著，且瞬时频率特征变化相似，揭示运动致疲劳过程中协同肌受中枢神经系统控制以更加同步的方式活动.Intermuscular coupling is defined as the interaction, correlation and coordination between different muscles during the body movement, which could be revealed by the synchronization analysis of surface electromyogram (sEMG). The multiscaled coherence analysis of sEMG signals could describe the multiple spatial and temporal functional connection characteristics of intermuscular coupling, which could be helpful for understanding the multiple spatial and temporal coupling mechanism of neuromuscular system. Furthermore, the coupling characteristics in frequency band of sEMG generally reflect the functional connection between muscles which relate to motion control and coordinative mechanism of the central nervous system (CNS). In this paper, we combine variational mode decomposition (VMD) and intermuscular coherence (IMC) analysis to propose a new method named VMD-IMC to quantitatively describe the muscular coupling characteristics in the corresponding frequency bands. First, sEMG data of flexor digitorum superficialis (FDS), flexor carpi ulnaris (FCU) and extensor digitorum (ED) are recorded simultaneously from twenty healthy subjects (253 years) who perform the designed grip task at sustained 20% maximum voluntary contraction under the static load. Then, the VMD approach is employed to adaptively decompose sEMG into several intrinsic mode functions to describe the information about different time-frequency scales. Furthermore, the coherence on different time-frequency scales between different sEMG signals is analyzed, and the significant coherent area index is calculated to quantitatively describe the functional coupling characteristics of the feature bands. And combining VMD with Hilbert transform, we calculate root mean square and mean instantaneous frequency (MIF) to describe the variations of energy and frequency of each muscle. The results show that coupling strengths increase with time, respectively, in beta (15-30 Hz) and gamma (30-45 Hz) band between two muscles (FDS vs FCU, FDS vs ED) during the sustained static force with low load. In addition, compared with the coupling between FDS and ED, the couplings between FDS and FCU in beta and gamma band under the condition of fatigue present more significant changes and similar trend in MIF variation with time. The obtained results reveal that the congenerous muscle is coordinated by CNS in a more synchronous way during the sustained isometric fatiguing contraction.
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Keywords:
- electromyogram /
- variational mode decomposition /
- coherence /
- intermuscular coupling
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[2] Enoka R M, Baudry S, Rudroff T 2011 J. Electromyogr. Kines. 21 208
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[14] Kattla S, Lowery M M 2010 Exp. Brain Res. 202 89
[15] Omlor W, Patino L, Hepp-Reymond M C 2007 Neuroimage 34 1191
[16] Baker S N, Olivier E, Lemon R N 1997 J. Physiol. 501 225
[17] Salenius S, Portin K, Kajola M 1997 J. Neurophysiol. 77 3401
[18] Danna-Dos Santos A, Poston B, Jesunathadas M 2010 J. Neurophysiol. 104 3576
[19] Gandevia S C 2001 Physiol. Rev. 81 1725
[20] Wang L J, Lu A Y, Zheng F H, Gong M X, Zhang L, Dong F 2014 China Sport Sci. 34 40 (in Chinese) [王乐军, 陆爱云, 郑樊慧, 龚铭新, 张磊, 董菲 2014 体育科学 34 40]
[21] de Luca C J 1997 J. Appl. Biomech. 13 135
[22] Lvnez M, Garland S J, Klass M 2008 J. Neurophysiol. 99 554
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[1] Baker S N 2007 Curr. Opin. Neurobiol. 17 649
[2] Enoka R M, Baudry S, Rudroff T 2011 J. Electromyogr. Kines. 21 208
[3] Grosse P, Cassidy M J, Brown P 2002 Clin. Neurophysiol. 113 1523
[4] Xie P, Song Y, Guo Z H, Chen X L, Wu X G, Su Y P, Du Y H 2016 J. Biomed. Eng. 33 244 (in Chinese) [谢平, 宋妍, 郭子晖, 陈晓玲, 吴晓光, 苏玉萍, 杜义浩 2016 生物医学工程学杂志 33 244]
[5] Patino L, Omlor W, Chakarov V 2008 J. Neurophysiol. 99 1906
[6] Charissou C, Vigouroux L, Berton E 2016 J. Electromyogr. Kines. 27 52
[7] Stamoulis C, Chang B S 2011 33rd Annual International Conference of the IEEE EMBS Boston, USA, August 30-September 3, 2011 p5908
[8] Wu Z, Huang N E 2009 Adv. Adapt. Data Analy. 1 1
[9] Dragomiretskiy K, Zosso D 2014 IEEE Trans. Signal Process. 62 531
[10] Xie P, Yang F M, Li X X, Yang Y, Chen X L, Zhang L T 2016 Acta Phys. Sin. 65 118701 (in Chinese) [谢平, 杨芳梅, 李欣欣, 杨勇, 陈晓玲, 张利泰 2016 65 118701]
[11] Lattimer L J, Lanovaz J L, Farthing J P 2016 J. Electromyogr. Kines. 30 231
[12] Xie H, Wang Z 2006 Comput. Meth. Prog. Biol. 82 114
[13] Rosenberg J R, Amjad A M, Breeze P 1989 Prog. Biophys. Mol. Biol. 53 1
[14] Kattla S, Lowery M M 2010 Exp. Brain Res. 202 89
[15] Omlor W, Patino L, Hepp-Reymond M C 2007 Neuroimage 34 1191
[16] Baker S N, Olivier E, Lemon R N 1997 J. Physiol. 501 225
[17] Salenius S, Portin K, Kajola M 1997 J. Neurophysiol. 77 3401
[18] Danna-Dos Santos A, Poston B, Jesunathadas M 2010 J. Neurophysiol. 104 3576
[19] Gandevia S C 2001 Physiol. Rev. 81 1725
[20] Wang L J, Lu A Y, Zheng F H, Gong M X, Zhang L, Dong F 2014 China Sport Sci. 34 40 (in Chinese) [王乐军, 陆爱云, 郑樊慧, 龚铭新, 张磊, 董菲 2014 体育科学 34 40]
[21] de Luca C J 1997 J. Appl. Biomech. 13 135
[22] Lvnez M, Garland S J, Klass M 2008 J. Neurophysiol. 99 554
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