Research progress on analysis methods in electroencephalography-electromyography synchronous coupling_Journal of Biomedical Engineering

Authors：

LI Sujiao ^1,2 , LIU Su ¹ , LAN He ¹ ,  YU Hongliu ^1,2,3

1. Institute of Rehabilitation Engineering and Technology, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China;
2. Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, P.R.China;
3. Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai 200093, P.R.China;

Corresponding author：

Keywords：

coherence analysis; Granger causality; mutual information; transfer entropy

DOI：

10.7507/1001-5515.201804005

Video：

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The motor nervous system transmits motion control information through nervous oscillations, which causes the synchronous oscillatory activity of the corresponding muscle to reflect the motion response information and give the cerebral cortex feedback, so that it can sense the state of the limbs. This synchronous oscillatory activity can reflect connectivity information of electroencephalography-electromyography (EEG-EMG) functional coupling. The strength of the coupling is determined by various factors including the strength of muscle contraction, attention, motion intention etc. It is very significant to study motor functional evaluation and control methods to analyze the changes of EEG-EMG synchronous coupling caused by different factors. This article mainly introduces and compares coherence and Granger causality of linear methods, the mutual information and transfer entropy of nonlinear methods in EEG-EMG synchronous coupling, and summarizes the application of each method, so that researchers in related fields can understand the current research progress on analysis methods of EEG-EMG synchronous systematically.

Citation： LI Sujiao, LIU Su, LAN He, YU Hongliu. Research progress on analysis methods in electroencephalography-electromyography synchronous coupling. Journal of Biomedical Engineering, 2019, 36(2): 334-337, 342. doi: 10.7507/1001-5515.201804005 Copy

1.	轩运动, 孙应飞, 黄志蓓, 等. 基于表面肌电信号的脑卒中上肢肌肉性能研究. 工程研究-跨学科视野中的工程, 2016, 8(6): 598-604.
2.	姜亚斌, 邹任玲, 刘建. 表面肌电信号的肌肉疲劳判别研究进展. 生物信息学, 2017, 15(2): 120-126.
3.	李毅, 刘晓峰, 王学友, 等. 基于人体表面肌电信号的装备座椅舒适度测试评价方法分析. 科技创新与应用, 2017, 22: 5-6.
4.	徐佳琳, 左国坤. 基于互信息与主成分分析的运动想象脑电特征选择算法. 生物医学工程学杂志, 2016, 33(2): 201-207.
5.	吴志敏. 不同模式下的 EEG-EMG 信号相干性和信息熵研究. 太原: 山西大学, 2015.
6.	Chwodhury A, Raza H, Dutta A, et al. A study on cortico-muscular coupling in finger motions for exoskeleton assisted Neuro-Rehabilitation//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015: 4610-4614.
7.	Xie Ping, Zhou Sa, Chen Xiaoling, et al. Estimation of corticomuscular coherence following stroke patients//2017 Chinese Automation Congress (CAC), Jinan, China: IEEE, 2017: 4263-4266.
8.	陈迎亚. 康复运动中脑肌电融合及同步分析方法研究. 秦皇岛: 燕山大学, 2016.
9.	Ushijima T, Sahroni A, Igasaki T, et al. Time-lapse changes in EEG-EMG coherence during weak voluntary contraction of the tibialis anterior muscle//10th Biomedical Engineering International Conference (BMEiCON). Sapporo, Japan: IEEE, 2017: 1-5.
10.	Zheng Yang, Gao Lin, Wang Gang, et al. The influence of unilateral contraction of hand muscles on the contralateral corticomuscular coherence during bimanual motor tasks. Neuropsychologia, 2016, 85: 199-207.
11.	Xu Yuhang, Mcclelland V M, Cvetkovic Z, et al. Delay estimation between EEG and EMG via coherence with time lag//2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China: IEEE, 2016, 2016: 734-738.
12.	高云园, 任磊磊, 张迎春, 等. 基于相干性的多频段脑肌电信号双向耦合分析. 传感技术学报, 2017, 30(10): 1465-1471.
13.	Granger C J. Some recent development in a concept of causality. Journal of Econometrics, 1988, 39(2): 199-211.
14.	Witham C L, Riddle C N, Baker M R, et al. Contributions of descending and ascending pathways to corticomuscular coherence in humans. Journal of Physiology-London, 2011, 589(15): 3789-3800.
15.	牛小辰. 康复运动中的脑肌电特征分析. 秦皇岛: 燕山大学, 2015.
16.	谢平, 陈迎亚, 张园园, 等. 基于 Gabor 小波和格兰杰因果的脑-肌电同步性分析. 中国生物医学工程学报, 2017, 36(1): 28-38.
17.	Shannon C E. The mathematical theory of communication. Bell Labs Tech J, 1950, 3(9): 31-32.
18.	Kim B, Kim L, Kim Y H, et al. Cross-association analysis of EEG and EMG signals according to movement intention state. Cogn Syst Res, 2017, 44: 1-9.
19.	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.
20.	Schreiber T. Measuring information transfer. Phys Rev Lett, 2000, 85(2): 461-464.
21.	赵化良. 基于传递熵的 MPCA 间歇过程监测方法. 计算机系统应用, 2016, 25(2): 146-151.
22.	So W K, Yang Lingling, Jelfs B, et al. Cross-Frequency information transfer from EEG to EMG in grasping//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)．Orlando, USA: IEEE, 2016: 4531-4534.
23.	张园园, 邹策, 陈晓玲, 等. 基于 Gabor 小波-传递熵的脑-肌电信号同步耦合分析. 生物医学工程学杂志, 2017, 34(6): 850-856.
24.	谢平, 杨芳梅, 陈晓玲, 等. 基于多尺度传递熵的脑肌电信号耦合分析. 物理学报, 2015, 64(24): 423-432.
25.	谢平, 杨芳梅, 李欣欣, 等. 基于变分模态分解-传递熵的脑肌电信号耦合分析. 物理学报, 2016, 65(11): 285-293.

1. 轩运动, 孙应飞, 黄志蓓, 等. 基于表面肌电信号的脑卒中上肢肌肉性能研究. 工程研究-跨学科视野中的工程, 2016, 8(6): 598-604.
2. 姜亚斌, 邹任玲, 刘建. 表面肌电信号的肌肉疲劳判别研究进展. 生物信息学, 2017, 15(2): 120-126.
3. 李毅, 刘晓峰, 王学友, 等. 基于人体表面肌电信号的装备座椅舒适度测试评价方法分析. 科技创新与应用, 2017, 22: 5-6.
4. 徐佳琳, 左国坤. 基于互信息与主成分分析的运动想象脑电特征选择算法. 生物医学工程学杂志, 2016, 33(2): 201-207.
5. 吴志敏. 不同模式下的 EEG-EMG 信号相干性和信息熵研究. 太原: 山西大学, 2015.
6. Chwodhury A, Raza H, Dutta A, et al. A study on cortico-muscular coupling in finger motions for exoskeleton assisted Neuro-Rehabilitation//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015: 4610-4614.
7. Xie Ping, Zhou Sa, Chen Xiaoling, et al. Estimation of corticomuscular coherence following stroke patients//2017 Chinese Automation Congress (CAC), Jinan, China: IEEE, 2017: 4263-4266.
8. 陈迎亚. 康复运动中脑肌电融合及同步分析方法研究. 秦皇岛: 燕山大学, 2016.
9. Ushijima T, Sahroni A, Igasaki T, et al. Time-lapse changes in EEG-EMG coherence during weak voluntary contraction of the tibialis anterior muscle//10th Biomedical Engineering International Conference (BMEiCON). Sapporo, Japan: IEEE, 2017: 1-5.
10. Zheng Yang, Gao Lin, Wang Gang, et al. The influence of unilateral contraction of hand muscles on the contralateral corticomuscular coherence during bimanual motor tasks. Neuropsychologia, 2016, 85: 199-207.
11. Xu Yuhang, Mcclelland V M, Cvetkovic Z, et al. Delay estimation between EEG and EMG via coherence with time lag//2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China: IEEE, 2016, 2016: 734-738.
12. 高云园, 任磊磊, 张迎春, 等. 基于相干性的多频段脑肌电信号双向耦合分析. 传感技术学报, 2017, 30(10): 1465-1471.
13. Granger C J. Some recent development in a concept of causality. Journal of Econometrics, 1988, 39(2): 199-211.
14. Witham C L, Riddle C N, Baker M R, et al. Contributions of descending and ascending pathways to corticomuscular coherence in humans. Journal of Physiology-London, 2011, 589(15): 3789-3800.
15. 牛小辰. 康复运动中的脑肌电特征分析. 秦皇岛: 燕山大学, 2015.
16. 谢平, 陈迎亚, 张园园, 等. 基于 Gabor 小波和格兰杰因果的脑-肌电同步性分析. 中国生物医学工程学报, 2017, 36(1): 28-38.
17. Shannon C E. The mathematical theory of communication. Bell Labs Tech J, 1950, 3(9): 31-32.
18. Kim B, Kim L, Kim Y H, et al. Cross-association analysis of EEG and EMG signals according to movement intention state. Cogn Syst Res, 2017, 44: 1-9.
19. 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.
20. Schreiber T. Measuring information transfer. Phys Rev Lett, 2000, 85(2): 461-464.
21. 赵化良. 基于传递熵的 MPCA 间歇过程监测方法. 计算机系统应用, 2016, 25(2): 146-151.
22. So W K, Yang Lingling, Jelfs B, et al. Cross-Frequency information transfer from EEG to EMG in grasping//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)．Orlando, USA: IEEE, 2016: 4531-4534.
23. 张园园, 邹策, 陈晓玲, 等. 基于 Gabor 小波-传递熵的脑-肌电信号同步耦合分析. 生物医学工程学杂志, 2017, 34(6): 850-856.
24. 谢平, 杨芳梅, 陈晓玲, 等. 基于多尺度传递熵的脑肌电信号耦合分析. 物理学报, 2015, 64(24): 423-432.
25. 谢平, 杨芳梅, 李欣欣, 等. 基于变分模态分解-传递熵的脑肌电信号耦合分析. 物理学报, 2016, 65(11): 285-293.

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