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.	軒運動, 孫應飛, 黃志蓓, 等. 基于表面肌電信號的腦卒中上肢肌肉性能研究. 工程研究-跨學科視野中的工程, 2016, 8(6): 598-604.
2.	姜亞斌, 鄒任玲, 劉建. 表面肌電信號的肌肉疲勞判別研究進展. 生物信息學, 2017, 15(2): 120-126.
3.	李毅, 劉曉峰, 王學友, 等. 基于人體表面肌電信號的裝備座椅舒適度測試評價方法分析. 科技創新與應用, 2017, 22: 5-6.
4.	徐佳琳, 左國坤. 基于互信息與主成分分析的運動想象腦電特征選擇算法. 生物醫學工程學雜志, 2016, 33(2): 201-207.
5.	吳志敏. 不同模式下的 EEG-EMG 信號相干性和信息熵研究. 太原: 山西大學, 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.	陳迎亞. 康復運動中腦肌電融合及同步分析方法研究. 秦皇島: 燕山大學, 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.	高云園, 任磊磊, 張迎春, 等. 基于相干性的多頻段腦肌電信號雙向耦合分析. 傳感技術學報, 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. 康復運動中的腦肌電特征分析. 秦皇島: 燕山大學, 2015.
16.	謝平, 陳迎亞, 張園園, 等. 基于 Gabor 小波和格蘭杰因果的腦-肌電同步性分析. 中國生物醫學工程學報, 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.	趙化良. 基于傳遞熵的 MPCA 間歇過程監測方法. 計算機系統應用, 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.	張園園, 鄒策, 陳曉玲, 等. 基于 Gabor 小波-傳遞熵的腦-肌電信號同步耦合分析. 生物醫學工程學雜志, 2017, 34(6): 850-856.
24.	謝平, 楊芳梅, 陳曉玲, 等. 基于多尺度傳遞熵的腦肌電信號耦合分析. 物理學報, 2015, 64(24): 423-432.
25.	謝平, 楊芳梅, 李欣欣, 等. 基于變分模態分解-傳遞熵的腦肌電信號耦合分析. 物理學報, 2016, 65(11): 285-293.

1. 軒運動, 孫應飛, 黃志蓓, 等. 基于表面肌電信號的腦卒中上肢肌肉性能研究. 工程研究-跨學科視野中的工程, 2016, 8(6): 598-604.
2. 姜亞斌, 鄒任玲, 劉建. 表面肌電信號的肌肉疲勞判別研究進展. 生物信息學, 2017, 15(2): 120-126.
3. 李毅, 劉曉峰, 王學友, 等. 基于人體表面肌電信號的裝備座椅舒適度測試評價方法分析. 科技創新與應用, 2017, 22: 5-6.
4. 徐佳琳, 左國坤. 基于互信息與主成分分析的運動想象腦電特征選擇算法. 生物醫學工程學雜志, 2016, 33(2): 201-207.
5. 吳志敏. 不同模式下的 EEG-EMG 信號相干性和信息熵研究. 太原: 山西大學, 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. 陳迎亞. 康復運動中腦肌電融合及同步分析方法研究. 秦皇島: 燕山大學, 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. 高云園, 任磊磊, 張迎春, 等. 基于相干性的多頻段腦肌電信號雙向耦合分析. 傳感技術學報, 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. 康復運動中的腦肌電特征分析. 秦皇島: 燕山大學, 2015.
16. 謝平, 陳迎亞, 張園園, 等. 基于 Gabor 小波和格蘭杰因果的腦-肌電同步性分析. 中國生物醫學工程學報, 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. 趙化良. 基于傳遞熵的 MPCA 間歇過程監測方法. 計算機系統應用, 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. 張園園, 鄒策, 陳曉玲, 等. 基于 Gabor 小波-傳遞熵的腦-肌電信號同步耦合分析. 生物醫學工程學雜志, 2017, 34(6): 850-856.
24. 謝平, 楊芳梅, 陳曉玲, 等. 基于多尺度傳遞熵的腦肌電信號耦合分析. 物理學報, 2015, 64(24): 423-432.
25. 謝平, 楊芳梅, 李欣欣, 等. 基于變分模態分解-傳遞熵的腦肌電信號耦合分析. 物理學報, 2016, 65(11): 285-293.

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