Gait event-driven dynamic changes in brain-muscle networks and neuromuscular coordination mechanisms_Journal of Biomedical Engineering

Authors：

LIU Xiaoguang ¹ , GUO Aoqi ¹ ,  LIANG Tie ^1,2 , LIU Xiuling ¹ ,  QIN Liang ³

1. College of Electronic Information Engineering, Hebei University, Baoding, Hebei 071002, P. R. China;
2. School of Information Technology, Guangxi Police College, Nanning 530028, P. R. China;
3. Division of Rehabilitation Medicine, ?The Affiliated Hospital of Hebei University, Baoding, Hebei 071002, P. R. China;

Corresponding?author：

LIANG Tie, Email: lanswer@163.com; QIN Liang, Email: qinlianghdfy@163.com

Keywords：

Brain-muscle network; Adaptive directed transfer function; Dynamic connection; Gait event

DOI：

10.7507/1001-5515.202412069

Video：

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Abstract Full text Figures/Tables Video References Cited by

Walking is a fundamental component of daily human activity, in which orderly execution of key gait events [such as heel strike (HS) and toe-off (TO) ] is essential for maintaining gait coordination and stability. However, the underlying brain–muscle neural coordination mechanisms associated with these events remain unclear. In this study, electroencephalography (EEG) signals from 19 channels and surface electromyography (sEMG) signals from 14 lower-limb muscles were synchronously recorded from 18 healthy participants during steady-state walking at a constant speed of 3.2 km/h. Brain–muscle connectivity networks were constructed using the adaptive directed transfer function (ADTF), and the dynamic connectivity characteristics associated with HS and TO events were quantitatively analyzed. The results showed that brain–muscle connectivity was strongest in the β frequency band. Intra-brain (EEG-EEG), brain-to-muscle (EEG-sEMG), and intra-muscle (sEMG-sEMG) connections were significantly stronger than muscle-to-brain (sEMG-EEG) connections. At both HS and TO events, information exchange between frontal-central cortical regions and the muscles of the supporting leg was markedly enhanced. Furthermore, compared with TO, the brain-muscle network at HS exhibited higher clustering coefficient, global efficiency, and betweenness centrality. These findings suggest that brain-muscle interactions during walking are predominantly mediated in the β band and dynamically modulated by gait events. Accurate characterization of gait event-related brain-muscle connectivity may provide important technical support for clarifying the neuromuscular coordination mechanisms underlying fine gait control.

Citation： LIU Xiaoguang, GUO Aoqi, LIANG Tie, LIU Xiuling, QIN Liang. Gait event-driven dynamic changes in brain-muscle networks and neuromuscular coordination mechanisms. Journal of Biomedical Engineering, 2026, 43(2): 267-276. doi: 10.7507/1001-5515.202412069 Copy

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2.	李冬, 張皓, 劉楠, 等. 認知-運動雙任務訓練對腦卒中恢復期患者平衡功能和步態效果的隨機對照試驗. 中國康復理論與實踐, 2024, 30(9): 1082-1091.
3.	Pazzaglia A, Bicanski A, Ferrario A, et al. Balancing central control and sensory feedback produces adaptable and robust locomotor patterns in a spiking, neuromechanical model of the salamander spinal cord. PLoS Comput Biol, 2025, 21(1): e1012101.
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8.	Kharb A, Saini V, Jain Y K, et al. A review of gait cycle and its parameters. Int J Comput Eng Manag, 2011, 13(1): 78-83.
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12.	Xu H, Xiong A. Advances and disturbances in sEMG-based intentions and movements recognition: a review. IEEE Sens J, 2021, 21(12): 13019-13028.
13.	Artoni F, Cometa A, Dalise S, et al. Cortico-muscular connectivity is modulated by passive and active Lokomat-assisted gait. Sci Rep, 2023, 13(1): 21618.
14.	Yi C, Qiu Y, Chen W, et al. Constructing time-varying directed EEG network by multivariate nonparametric dynamical Granger causality. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1412-1421.
15.	Astolfi L, Babiloni F. The instantaneous estimation of the time-varying cortical connectivity by adaptive multivariate estimators//Estimation of cortical connectivity in humans: advanced signal processing techniques. Cham: Springer, 2022: 69-81.
16.	Xu F, Wang Y, Li H, et al. Time-varying effective connectivity for describing the dynamic brain networks of post-stroke rehabilitation. Front Aging Neurosci, 2022, 14: 911513.
17.	Liu Y, Yu S, Li J, et al. Brain state and dynamic transition patterns of motor imagery revealed by the Bayes hidden Markov model. Cogn Neurodyn, 2024, 18(5): 2455-2470.
18.	Wilke C, Ding L, He B. Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function. IEEE Trans Biomed Eng, 2008, 55(11): 2557-2564.
19.	Zhang X, Zhang S, Lu B, et al. Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis. J Neural Eng, 2022, 19(3): 036014.
20.	Arnold M, Milner X H R, Witte H, et al. Adaptive AR modeling of nonstationary time series by means of Kalman filtering. IEEE Trans Biomed Eng, 1998, 45(5): 553-562.
21.	Li F, Chen B, Li H, et al. The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(7): 725-733.
22.	Kang J, Mao W, Wu J, et al. Development of EEG connectivity from preschool to school-age children. Front Neurosci, 2024, 17: 1277786.
23.	Kaminski M J, Blinowska K J. A new method of the description of the information flow in the brain structures. Biol Cybern, 1991, 65(3): 203-210.
24.	Zhang J J, Bai Z, Fong K N K. Resting-state cortical electroencephalogram rhythms and network in patients after chronic stroke. J Neuroeng Rehabil, 2024, 21(1): 32.
25.	Yuan M, Zhao X. Asymptotic distributions of the average clustering coefficient and its variant. Filomat, 2025, 39(15): 5297-5334.
26.	Zhang Q, Deng R, Ding K, et al. Structural analysis and the sum of nodes’ betweenness centrality in complex networks. Chaos Solitons Fractals, 2024, 185: 115158.
27.	Erceg-Hurn D M, Mirosevich V M. Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. Am Psychol, 2008, 63(7): 591-601.
28.	Cohen J C. Anatomy and biomechanical aspects of the gastrocsoleus complex. Foot Ankle Clin, 2009, 14(4): 617-626.
29.	Zandvoort C S, van Die?n J H, Dominici N, et al. The human sensorimotor cortex fosters muscle synergies through cortico-synergy coherence. Neuroimage, 2019, 199: 30-37.
30.	Atanas A A, Kim J, Wang Z, et al. Brain-wide representations of behavior spanning multiple timescales and states in C. elegans. Cell, 2023, 186(19): 4134-4151.
31.	Gao X, Jie T, Xu D, et al. Adaptive adjustments in lower limb muscle coordination during single-leg landing tasks in Latin dancers. Biomimetics, 2024, 9(8): 489.
32.	Minkes-Weiland S, Houdijk H, Floor S, et al. Effects of restraining forces on propulsion and other gait characteristics during treadmill walking post-stroke. Clin Biomech, 2025, 124: 106664.
33.	Aout T, Begon M, Peyrot N, et al. Société de Biomécanique young investigator award 2022: effects of applying functional electrical stimulation to ankle plantarflexor muscles on forward propulsion during walking in young healthy adults. J Biomech, 2024, 168: 112114.
34.	Skvortsov D V, Khudaigulova A R, Kaurkin S N, et al. Biofeedback training for knee joint range of motion in patients with ischemic cerebral stroke: a pilot study. Phys Rehabil Med Med Rehabil, 2025, 7(2): 109-124.
35.	Dadfar M, Soltani M, Novinzad M B, et al. Lower extremity energy absorption strategies at different phases during single and double-leg landings with knee valgus in pubertal female athletes. Sci Rep, 2021, 11(1): 17516.
36.	Chen L, Zhang L, Wang Z, et al. Task-related reconfiguration patterns of frontoparietal network during motor imagery. Neuroscience, 2025, 564: 183-194.
37.	Phang C R, Su K H, Cheng Y Y, et al. Time synchronization between parietal–frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks. J Neuroeng Rehabil, 2024, 21(1): 101.
38.	Petersen T H, Willerslev-Olsen M, Conway B A, et al. The motor cortex drives the muscles during walking in human subjects. J Physiol, 2012, 590(10): 2443-2452.
39.	Fan P, Kim Y, Han D W, et al. Alterations in the neuromuscular control mechanism of the legs during a post-fatigue landing make the lower limbs more susceptible to injury. Bioengineering, 2025, 12(3): 233.
40.	Severini G, Zych M. Locomotor adaptations: paradigms, principles and perspectives. Prog Biomed Eng, 2022, 4(4): 042003.
41.	Lin J Z, Lin Y A, Tai W H, et al. Influence of landing in neuromuscular control and ground reaction force with ankle instability: a narrative review. Bioengineering, 2022, 9(2): 68.

1. Febrer-Nafría M, Nasr A, Ezati M, et al. Predictive multibody dynamic simulation of human neuromusculoskeletal systems: a review. Multibody Syst Dyn, 2023, 58(3): 299-339.
2. 李冬, 張皓, 劉楠, 等. 認知-運動雙任務訓練對腦卒中恢復期患者平衡功能和步態效果的隨機對照試驗. 中國康復理論與實踐, 2024, 30(9): 1082-1091.
3. Pazzaglia A, Bicanski A, Ferrario A, et al. Balancing central control and sensory feedback produces adaptable and robust locomotor patterns in a spiking, neuromechanical model of the salamander spinal cord. PLoS Comput Biol, 2025, 21(1): e1012101.
4. Takakusaki K. Neurophysiology of gait: from the spinal cord to the frontal lobe. Mov Disord, 2013, 28(11): 1483-1491.
5. Bartels A L, Leenders K L. Brain imaging in patients with freezing of gait. Mov Disord, 2008, 23(S2): S461-S467.
6. Artoni F, Fanciullacci C, Bertolucci F, et al. Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking. Neuroimage, 2017, 159: 403-416.
7. Jankovic J. Gait disorders. Neurol Clin, 2015, 33(1): 249-268.
8. Kharb A, Saini V, Jain Y K, et al. A review of gait cycle and its parameters. Int J Comput Eng Manag, 2011, 13(1): 78-83.
9. Zeni J A, Richards J G, Higginson J S. Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait Posture, 2008, 27(4): 710-714.
10. Richer N, Bradford J C, Ferris D P. Mobile neuroimaging: what we have learned about the neural control of human walking, with an emphasis on EEG-based research. Neurosci Biobehav Rev, 2024, 162: 105718.
11. Majhi S, Ghosh S, Pal P K, et al. Patterns of neuronal synchrony in higher-order networks. Phys Life Rev, 2025, 52: 144-170.
12. Xu H, Xiong A. Advances and disturbances in sEMG-based intentions and movements recognition: a review. IEEE Sens J, 2021, 21(12): 13019-13028.
13. Artoni F, Cometa A, Dalise S, et al. Cortico-muscular connectivity is modulated by passive and active Lokomat-assisted gait. Sci Rep, 2023, 13(1): 21618.
14. Yi C, Qiu Y, Chen W, et al. Constructing time-varying directed EEG network by multivariate nonparametric dynamical Granger causality. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1412-1421.
15. Astolfi L, Babiloni F. The instantaneous estimation of the time-varying cortical connectivity by adaptive multivariate estimators//Estimation of cortical connectivity in humans: advanced signal processing techniques. Cham: Springer, 2022: 69-81.
16. Xu F, Wang Y, Li H, et al. Time-varying effective connectivity for describing the dynamic brain networks of post-stroke rehabilitation. Front Aging Neurosci, 2022, 14: 911513.
17. Liu Y, Yu S, Li J, et al. Brain state and dynamic transition patterns of motor imagery revealed by the Bayes hidden Markov model. Cogn Neurodyn, 2024, 18(5): 2455-2470.
18. Wilke C, Ding L, He B. Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function. IEEE Trans Biomed Eng, 2008, 55(11): 2557-2564.
19. Zhang X, Zhang S, Lu B, et al. Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis. J Neural Eng, 2022, 19(3): 036014.
20. Arnold M, Milner X H R, Witte H, et al. Adaptive AR modeling of nonstationary time series by means of Kalman filtering. IEEE Trans Biomed Eng, 1998, 45(5): 553-562.
21. Li F, Chen B, Li H, et al. The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(7): 725-733.
22. Kang J, Mao W, Wu J, et al. Development of EEG connectivity from preschool to school-age children. Front Neurosci, 2024, 17: 1277786.
23. Kaminski M J, Blinowska K J. A new method of the description of the information flow in the brain structures. Biol Cybern, 1991, 65(3): 203-210.
24. Zhang J J, Bai Z, Fong K N K. Resting-state cortical electroencephalogram rhythms and network in patients after chronic stroke. J Neuroeng Rehabil, 2024, 21(1): 32.
25. Yuan M, Zhao X. Asymptotic distributions of the average clustering coefficient and its variant. Filomat, 2025, 39(15): 5297-5334.
26. Zhang Q, Deng R, Ding K, et al. Structural analysis and the sum of nodes’ betweenness centrality in complex networks. Chaos Solitons Fractals, 2024, 185: 115158.
27. Erceg-Hurn D M, Mirosevich V M. Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. Am Psychol, 2008, 63(7): 591-601.
28. Cohen J C. Anatomy and biomechanical aspects of the gastrocsoleus complex. Foot Ankle Clin, 2009, 14(4): 617-626.
29. Zandvoort C S, van Die?n J H, Dominici N, et al. The human sensorimotor cortex fosters muscle synergies through cortico-synergy coherence. Neuroimage, 2019, 199: 30-37.
30. Atanas A A, Kim J, Wang Z, et al. Brain-wide representations of behavior spanning multiple timescales and states in C. elegans. Cell, 2023, 186(19): 4134-4151.
31. Gao X, Jie T, Xu D, et al. Adaptive adjustments in lower limb muscle coordination during single-leg landing tasks in Latin dancers. Biomimetics, 2024, 9(8): 489.
32. Minkes-Weiland S, Houdijk H, Floor S, et al. Effects of restraining forces on propulsion and other gait characteristics during treadmill walking post-stroke. Clin Biomech, 2025, 124: 106664.
33. Aout T, Begon M, Peyrot N, et al. Société de Biomécanique young investigator award 2022: effects of applying functional electrical stimulation to ankle plantarflexor muscles on forward propulsion during walking in young healthy adults. J Biomech, 2024, 168: 112114.
34. Skvortsov D V, Khudaigulova A R, Kaurkin S N, et al. Biofeedback training for knee joint range of motion in patients with ischemic cerebral stroke: a pilot study. Phys Rehabil Med Med Rehabil, 2025, 7(2): 109-124.
35. Dadfar M, Soltani M, Novinzad M B, et al. Lower extremity energy absorption strategies at different phases during single and double-leg landings with knee valgus in pubertal female athletes. Sci Rep, 2021, 11(1): 17516.
36. Chen L, Zhang L, Wang Z, et al. Task-related reconfiguration patterns of frontoparietal network during motor imagery. Neuroscience, 2025, 564: 183-194.
37. Phang C R, Su K H, Cheng Y Y, et al. Time synchronization between parietal–frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks. J Neuroeng Rehabil, 2024, 21(1): 101.
38. Petersen T H, Willerslev-Olsen M, Conway B A, et al. The motor cortex drives the muscles during walking in human subjects. J Physiol, 2012, 590(10): 2443-2452.
39. Fan P, Kim Y, Han D W, et al. Alterations in the neuromuscular control mechanism of the legs during a post-fatigue landing make the lower limbs more susceptible to injury. Bioengineering, 2025, 12(3): 233.
40. Severini G, Zych M. Locomotor adaptations: paradigms, principles and perspectives. Prog Biomed Eng, 2022, 4(4): 042003.
41. Lin J Z, Lin Y A, Tai W H, et al. Influence of landing in neuromuscular control and ground reaction force with ankle instability: a narrative review. Bioengineering, 2022, 9(2): 68.

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Gait event-driven dynamic changes in brain-muscle networks and neuromuscular coordination mechanisms

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