基于表面肌电分解的皮层肌肉耦合机理研究

席旭刚; 王成浩; 汪婷; 孔万增; 厉力华

doi:10.12263/DZXB.20221099

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基于表面肌电分解的皮层肌肉耦合机理研究

学术论文 | 更新时间：2025-12-08

- 基于表面肌电分解的皮层肌肉耦合机理研究
- Mechanism of Corticomuscular Coupling Based on Surface Electromyography Decomposition
- 电子学报 2024年52卷第8期页码：2607-2617
- 作者机构：
  
  1.杭州电子科技大学自动化学院,浙江杭州 310018
  2.浙江省脑机协同重点实验室,浙江杭州 310018
  3.杭州电子科技大学计算机学院,浙江杭州 310018
- 作者简介：
  
  [ "席旭刚男，1975年出生，浙江金华人.现为杭州电子科技大学博士生导师、硕士生导师.主要研究方向为脑机接口与混合智能、人机共融机器人、智能医疗器械、智能检测等. E-mail: xixugang@hdu.edu.cn" ]
  [ "王成浩男，1997 年出生，山西太原人 . 现为杭州电子科技大学硕士研究生 . 研究方向为脑机接口与混合智能. E-mail: 601011054@qq.com" ]
  [ "汪婷女，1990年出生，浙江金华人.现为杭州电子科技大学硕士生导师.主要研究方向为生物医学信号处理、脑机接口、智能医疗仪器. E-mail: tingwang@hdu.edu.cn" ]
  [ "孔万增男，1980年出生.现为杭州电子科技大学博士生导师、硕士生导师.主要研究方向为人工智能与模式识别、嵌入式可穿戴计算、脑机交互与认知计算等.中国电子学会会员编号：E190008733S.E-mail: kongwanzeng@hdu.edu.cn" ]
  [ "厉力华男，1965年出生，浙江东阳人.现为杭州电子科技大学博士生导师、硕士生导师.主要研究方向为生物医学、智能诊断等. lilh@hdu.edu.cn" ]
- 基金信息：
  
  科技部2030重大项目(2021ZD0113204);国家自然科学基金(U20B2074;61971169);浙江省重点研发计划(2021C03031)
- DOI：10.12263/DZXB.20221099
  中图分类号： TN911.7;R318
- 收稿：2022-09-27，
  
  修回：2023-03-30，
  
  纸质出版：2024-08-25
- 稿件说明：
移动端阅览
席旭刚, 王成浩, 汪婷, 等. 基于表面肌电分解的皮层肌肉耦合机理研究[J]. 电子学报, 2024, 52(08): 2607-2617.

XI Xu-gang, WANG Cheng-hao, WANG Ting, et al. Mechanism of Corticomuscular Coupling Based on Surface Electromyography Decomposition[J]. Acta Electronica Sinica, 2024, 52(08): 2607-2617.
席旭刚, 王成浩, 汪婷, 等. 基于表面肌电分解的皮层肌肉耦合机理研究[J]. 电子学报, 2024, 52(08): 2607-2617. DOI：10.12263/DZXB.20221099

XI Xu-gang, WANG Cheng-hao, WANG Ting, et al. Mechanism of Corticomuscular Coupling Based on Surface Electromyography Decomposition[J]. Acta Electronica Sinica, 2024, 52(08): 2607-2617. DOI：10.12263/DZXB.20221099

摘要

基于表面肌电信号分解还原了肌电信号最原始的组成成分，通过分解后肌电信号段的特征研究神经肌肉系统中脑肌电信息传递规律，可以从生物电信息传递机理探索人体运动的本质.本文分别采集了9名受试者最大抓握力量的15%和30%（15%MVC、30%MVC）所对应的EEG（ElectroEncephaloGraph）和sEMC（surface ElectroMyoGraphy）信号，以形态学分解为基础对sEMG信号进行模板重构分解，获得运动单元动作电位MUAP（Motion Unit Action Potential），提取MUAP的幅值、数量和发射速率作为特征，基于该类特征与同步脑电信号的变化趋势以及传递熵值探索大脑皮层与肌肉的信息传递规律.不同抓握力量水平下，30%MVC提取的3个特征均比15% MVC的数值更显著，但两种力量水平提取的3个特征随同步脑电变化趋势相同：当EEG信号形成波峰或波谷信号时，MUAP数量、幅值和发射速率特征均呈现增加的变化趋势，其中MUAP幅值的增加趋势最为明显，且MUAP幅值特征与同步EEG信号的耦合（TE传递熵值）效果最好.虽然力量水平的不同会影响脑肌电信号强弱，但总体呈现的信息传递规律是一致的：当肢体肌肉收缩脑电信号增强而形成波峰或波谷时，MUAP数量、幅值和发射速率3个特征值均呈现上升的变化趋势，其中MUAP幅值特征响应效果最好，该特征能较好体现人体运动控制过程中神经肌肉系统中的信息传递规律.

Abstract

Based on the decomposition of surface EMG (ElectroMyoGraphy) signals

the most primitive components of EMG signals are restored. By analyzing the features of the decomposed sEMG (Surface ElectroMyoGraphy) signals

the EEG (ElectroEncephaloGraph) information transmission law in neuromuscular system can be studied

and the bioelectric information transmission mechanism can be explored from the essence of human motion. The EEG and sEMG signals corresponding to 15% MVC and 30% MVC of 9 subjects are collected. Then

the motion unit action potential (MUAP) is obtained by STA template reconstruction decomposition of sEMG based on morphological decomposition. Then

the amplitude of MUAP

the number of MUAP and the firing rate are extracted as features

based on these features the trend of synchronous EEG signals and the transmission entropy value

the information transmission law of cortical muscles is explored. Result Under different strength levels

the three features under 30% MVC are larger than those under 15% MVC

but the change trends of the three features are same. When the EEG signal forms a peak or valley signal

MUAP Number

Amp and firing rate all show a trend of increasing. The increasing trend of MUAP amplitude is the most obvious

and the coupling effect of MUAP amplitude features and synchronous EEG signals is the best (transmission entropy value). Conclusion although different strength levels affect the strength of the extracted EEG signals

the overall information transfer law is consistent: when the intensity of muscle contraction EEG signals is enhanced to form peak or trough signals

the three features show an upward trend

and the MUAP amplitude feature is the best response among the three selected features

that is

this feature can better reflect the information transmission law of neuromuscular system in the process of human motion control.

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references

成娟 , 陈勋 , 彭虎 . 基于样本熵的肌电信号起始点检测研究 [J ] . 电子学报 , 2016 , 44 ( 2 ): 479 - 484 .

CHENG J , CHEN X , PENG H . An onset detection method for action surface electromyography based on sample entropy [J ] . Acta Electonica Sinica , 2016 , 44 ( 2 ): 479 - 484 . (in Chinese)

SHI J , CHANG Q , ZHENG Y P . Feasibility of controlling prosthetic hand using sonomyography signal in real time: Preliminary study [J ] . Journal of Rehabilitation Research & Development , 2010 , 47 ( 2 ): 87 - 98 .

常文文 , 闫光辉 , 杨志飞 , 等 . 基于脑电熵值特征和功能连接的不同线型道路下驾驶状态检测 [J ] . 电子学报 , 2023 , 51 ( 10 ): 2874 - 2883 .

CHANG W W , YAN G H , YANG Z F , et al . Detection of driving state under different curve road based on entropy and functional connectivity of EEG [J ] . Acta Electonica Sinica , 2023 , 51 ( 10 ): 2874 - 2883 . (in Chinese)

LIU J , SHENG Y , LIU H . Corticomuscular coherence and its applications: A review [J ] . Frontiers in Human Neuroscience , 2019 , 13 : 100 .

GROSSE P , BROWN P . Corticomuscular and intermuscular frequency analysis [J ] . Supplements to Clinical Neurophysiology , 2004 , 57 : 570 - 576 .

GWIN J T , FERRIS D P . Beta-and gamma-range human lower limb corticomuscular coherence [J ] . Frontiers in Human Neuroscience , 2012 , 6 : 258 .

YANG Q , SIEMIONOW V , YAO W , et al . Single-trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling [J ] . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2010 , 18 ( 2 ): 97 - 106 .

YAO B , KLEIN C S , HU H , et al . Motor unit properties of the first dorsal interosseous in chronic stroke subjects: Concentric needle and single fiber EMG analysis [J ] . Frontiers in Physiology , 2018 , 9 : 393754 .

SIQUEIRA JUNIORR A L D , SOARES A B . A novel method for EMG decomposition based on matched filters [J ] . Research on Biomedical Engineering , 2015 , 31 ( 1 ): 44 - 55 .

NAWAB S H , CHANG S S , DE LUCA C J . High-yield decomposition of surface EMG signals [J ] . Clinical Neurophysiology , 2010 , 121 ( 10 ): 1602 - 1615 .

HOLOBAR A , ZAZULA D . Correlation-based decomposition of surface electromyograms at low contraction forces [J ] . Medical and Biological Engineering and Computing , 2004 , 42 : 487 - 495 .

DE LUCA C J , ROY A M , Erim Z . Synchronization of motor-unit firings in several human muscles [J ] . Journal of Neurophysiology , 1993 , 70 ( 5 ): 2010 - 2023 . DOI: 10.1152/jn.1993.70.5.2010 http://dx.doi.org/10.1152/jn.1993.70.5.2010 .

DE LUCA C J , ADAM A , WOTIZ R , et al . Decomposition of surface EMG signals [J ] . Journal of Neurophysiology , 2006 , 96 ( 3 ): 1646 - 1657 .

JUNIOR J J A M , FREITAS M L B , SIQUEIRA H V , et al . Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach [J ] . Biomedical Signal Processing and Control , 2020 , 59 : 101920 .

DING Q , ZHAO X , HAN J , et al . Adaptive hybrid classifier for myoelectric pattern recognition against the interferences of outlier motion, muscle fatigue, and electrode doffing [J ] . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2019 , 27 ( 5 ): 1071 - 1080 .

HU X , RYMER W Z , Suresh N L . Assessment of validity of a high-yield surface electromyogram decomposition [J ] . Journal of Neuroengineering and Rehabilitation , 2013 , 10 : 1 - 12 .

BIAGETTI G , CRIPPA P , ORCIONI S , et al . Homomorphic deconvolution for MUAP estimation from surface EMG signals [J ] . IEEE Journal of Biomedical and Health Informatics , 2016 , 21 ( 2 ): 328 - 338 .

罗万国 , 侯文生 , 郑小林 , 等 . 基于先验模板的表面肌电信号渐进分解算法研究 [J ] . 中国生物医学工程学报 , 2014 , 31 ( 3 ): 366 - 372 .

LUO W G , HOU W S , ZHENG X L , et al . Research on progressive decomposition algorithm of surface electromyography signal based on prior template [J ] . Chinese Journal of Biomedical Engineering , 2014 , 31 ( 3 ): 366 - 372 . (in Chinese)

CHEN M , HOLOBAR A , ZHANG X , et al . Progressive FastICA peel‐off and convolution kernel compensation demonstrate high agreement for high density surface EMG decomposition [J ] . Neural Plasticity , 2016 , 2016 ( 1 ): 3489540 .

SHI X , ZHU J Q , QIN P J , et al . Feature extraction method of lower limb surface EMG signal based on improved energy nucleus [J ] . Chinese Journal of Scientific Instrument , 2020 , 41 ( 1 ): 121 - 128 .

CHEN X , ZHANG Y , CHENG S , et al . Transfer spectral entropy and application to functional corticomuscular coupling [J ] . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2019 , 27 ( 5 ): 1092 - 1102 .

CHUNLI W , CHUNLEI Z . Denoising algorithm based on wavelet adaptive threshold [J ] . Physics Procedia , 2012 , 24 : 678 - 685 .

LI Y , LI L , ZHENG X . EEG-EMG coherence analysis of different hand motions in healthy subjects [J ] . Journal of Biomedical Engineering , 2014 , 31 ( 5 ): 962 - 966 .

BAWA P N S , JONES K E , STEIN R B . Assessment of size ordered recruitment [J ] . Frontiers in Human Neuroscience , 2014 , 8 : 532 .

黄晓凤 , 裴晨卉 , 梁战华 . 感觉运动整合与局灶性肌张力障碍 [J ] . 中华神经科杂志 , 2016 , 49 ( 2 ): 157 - 160 .

HUANG X F , PEI C H , LIANG Z H . Sensory motor integration and focal dystonia [J ] . Chinese Journal of Neurology , 2016 , 49 ( 2 ): 157 - 160 . (in Chinese)

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