|
|
|
Multichannel EEG-EMG Coupling Analysis
Using aVariable Scale Symbolic Transfer Entropy Approach |
| Gao Yunyuan1*, Ren Leilei1, Zhou Xu1,2 ,Zhang Qizhong1, Zhang Yingchun2,3* |
1Intelligent Control & Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
2Guangdong Provincial Work-Injury Rehabilitation Hospital, Guangzhou 510900, China;
3Department of Biomedical Engineering, University of Houston, Houston 77204, Texas, USA |
|
|
|
|
Abstract The coupling strength (CS) of the electroencephalogram (EEG) and electromyogram (EMG) reflects the connection between the cerebral cortex and active muscles in motion control. Conventional symbolic transfer entropy analysis (STE) methods are limited to effectively reveal the dynamic characteristics. In order to quantitatively analyze multi-channel EEG and EMG coupling, a variable scale symbolic transfer entropy (VSP-STE) analysis approach was proposed in this paper, and a CS expressive method was presented to quantitatively measure the corticomuscular CS of EEG and EMG signals during grip tasks. The influence of the scale parameter on EEG-EMG transfer entropy was first analyzed and compared at different grip force levels. The optimized scale parameter was then applied to the STE calculation in experimental data analysis. Results demonstrated the dominance of the C3 and C4 EEG channels in motion control, as well as the contralateral control mechanism of the brain. In addition, the premotor cortex (FC5 and FC6) also played an important role in corticomuscular coupling. Results showed that the EMG→EEG transfer entropy increased as the grip force increased (5, 10, 20 kg), and the right hand (dominant hand) showed higher EEG→EMG transfer entropy than the left hand in all the 3 subjects. The two-way (EEG→EMG, EMG→EEG) coupling strengths were also increased as the grip force increased. The statistical analysis showed that at 5, 10, 20 kg grip force levels, EMG→EEG coupling strength of left hand was 0.0330±0.0058,0.0373±0.0040, 0.0451±0.0055 respectively, and that of right hand was 0.0352±0.0029, 0.0432±0.0035, 0.0603±0.0018 respectively. There was a significant difference (p<0.05) of coupling strength, except for that between 5 kg and 10 kg of grip force in left hand. For EEG→EMG, coupling strength of left hand is 0.0253±0.0047, 0.0379±0.0026, 0.0481±0.0068, and that of right hand is 0.0333±0.0041, 0.0510±0.0057, and 0.0649±0.0085 respectively. The difference of coupling strength was significant for all. In conclusion, our results show that corticomuscular coupling is a two-way process and the coupling strength varies with different channels and gripping strength. VSP-STE can be used to describe the nonlinear synchronizing characteristics and information interaction of the cerebral cortex and neuromuscular tissue.
|
|
Received: 23 May 2017
|
|
|
|
|
|
[1] Bartsch R, Kantelhardt JW, Penzel T, et al. Experimental evidence for phase synchronization transitions in the human cardiorespiratory system[J]. Physical Review Letters, 2007, 98(5):054102.
[2] Pfurtscheller G, Fh LDS. Event-related EEG/MEG synchronization and desynchronization basic principles[J]. Clinical Neurophysiology,1999, 110(11):1842-1857.
[3] Poortvliet PC, Tucker KJ, Finnigan S, et al. Cortical activity differs between position- and force-control knee extension tasks[J]. Experimental Brain Research, 2015,233(12):3447-3457.
[4] Von CK, Bayraktaroglu Z, Hohlefeld FU, et al. Corticomuscular coherence in acute and chronic stroke.[J]. Clinical Neurophysiology, 2014, 125(6):1182-1191.
[5] 荣瑶. 大脑皮层与上肢肌肉间功能耦合与信息传输的研究[D]. 北京:北京工业大学, 2013.
[6] 吴东宇, 刘霖, 宋玖骏,等. 脑电非线性分析评价卒中患者的意识障碍[J]. 中国脑血管病杂志, 2008, 5(9):385-389.
[7] 吕金虎, 陆君安, 陈士华. 混沌时间序列分析及其应用[M]. 武汉:武汉大学出版社, 2002.
[8] Kanazawa H, Kawai M, Kinai T, et al.Cortical muscle control of spontaneous movements in human neonates[J]. European Journal of Neuroscience,2014,40(3):2548-2553.
[9] Tomasevic L,Zito G, Pasqualetti P, et al. Cortico-muscular coherence as an index of fatigue in multiple sclerosis.[J]. Multiple Sclerosis Journal,2013,19(3):334-343.
[10] Hu Sanqing, Wang Hui, Zhang Jianhai, et al. Comparison analysis: Granger causality and new causality and their applications to motor imagery[J]. IEEE Transactions on Neural Networks & Learning Systems, 2016, 27(7):1429-1444.
[11] 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[J]. 物理学报, 2015(24):419-428.
[12] Vicente R, Wibral M, Lindner M, et al. Transfer entropy—A model-free measure of effective connectivity for the neurosciences[J]. Journal of Computational Neuroscience, 2011, 30(1):45-67.
[13] 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[J]. 物理学报,2016,65(11):277-285.
[14] Staniek M,Lehnertz K. Symbolic transfer entropy.[J]. Physical Review Letters,2008, 100(15):3136-3140.
[15] Omlor W, Patino L, Heppreymond M C, et al. Gamma-range corticomuscular coherence during dynamic force output.[J]. Neuroimage, 2007, 34(3):1191-1198.
[16] Shen Wei, Wang Jun. Time irreversibility analysis of ECG based on symbolic relative entropy[J]. Acta Physica Sinica, 2011,60(11): 2509-2515.
[17] 张梅, 崔超, 马千里,等. 基于符号化部分互信息熵的多参数生物电信号的耦合分析[J]. 物理学报, 2013(6):68704-068704.
[18] 姚文坡, 刘铁兵, 戴加飞,等. 脑电信号的多尺度排列熵分析[J]. 物理学报, 2014, 63(7):78704-078704.
[19] Shao Shengjia, Guo Ce, Luk W, et al. Accelerating transfer entropy computation[C]// International Conference on Field-Programmable Technology. Shanghai: IEEE, 2015:60-67.
[20] 王俊,余正锋. Symbolic transfer entropy-based premature signal analysis[J]. Chinese Physics B,2012,21(1):18702-18705.
[21] Witham C L, Riddle CN, Baker MR, et al. Contributions of descending and ascending pathways to corticomuscular coherence in humans[J]. Journal of Physiology, 2011, 589(15):3789-3800.
[22] Rojas H, Ramos M, Benaim G, et al. Synchronous cortical oscillatory activity during motor action[J]. Current Opinion in Neurobiology, 2003, 13(6):678-684.
[23] Brown P. Cortical drives to human muscle: The piper and related rhythms[J]. Progress in Neurobiology, 2000, 60(1):97-108.
[24] 汤晓军. 上肢运动调控的神经机制研究[D]. 天津:南开大学, 2009.
[25] 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[J]. 中国生物医学工程学报, 2017, 36(1):28-38. |
|
|
|
