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Upper Limb Fatigue Information Variation Analysis Based on Parallel Brain and Muscle Functional Networks.

Xiaoguang Liu1,2, Pengyuan Lin3,4, Aoqi Guo3,4

  • 1College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China. lxg_hbu@163.com.

Annals of Biomedical Engineering
|March 19, 2026
PubMed
Summary
This summary is machine-generated.

Muscle fatigue alters brain and muscle network topology. This study analyzed surface electromyography (sEMG) and electroencephalography (EEG) signals to reveal system-level changes in neuromuscular control during fatigue.

Keywords:
Complex networkElectroencephalographyFunctional networkMuscle fatigueSurface electromyography

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Systems Physiology

Background:

  • Muscle fatigue involves coordinated neuromuscular and cortical changes.
  • Traditional fatigue assessment using single signals misses system-level brain-muscle network reorganization.
  • Characterizing these network dynamics is crucial for understanding fatigue.

Purpose of the Study:

  • To characterize upper limb muscle fatigue by analyzing parallel functional networks from surface electromyography (sEMG) and electroencephalography (EEG) signals.
  • To investigate system-level reorganization of the brain-muscle control network during fatigue.

Main Methods:

  • Sixteen healthy participants performed isometric elbow flexion tasks under non-fatigued and fatigued conditions.
  • Simultaneous multichannel sEMG and EEG recordings were analyzed.
  • Functional muscle and brain networks were constructed using generalized partial directed coherence, with network topology quantified by average clustering coefficient (ACC), average global efficiency (AGE), and average shortest path length (APL).

Main Results:

  • Muscle functional networks showed increased ACC and AGE, with reduced APL, indicating enhanced local clustering and efficient information transfer.
  • Brain functional networks exhibited significant changes in the beta-band, with increased ACC and AGE and decreased APL.
  • Gamma-band network metrics showed limited alterations, suggesting fatigue-related adaptations are reflected in network topology.

Conclusions:

  • A parallel brain and muscle functional network framework provides system-level characterization of upper limb muscle fatigue.
  • This approach captures fatigue-related network topology changes, offering a system-level reference for neuromuscular coordination studies.
  • It lays a methodological foundation for future rehabilitation-oriented fatigue monitoring.