Scholarlink

Purpose <p>Muscle fatigue is accompanied by coordinated changes in both neuromuscular activation and cortical activity. Traditional fatigue assessment methods based on single physiological signals often fail to capture system-level reorganization of the brain–muscle control network. This study aims to characterize upper limb muscle fatigue by analyzing parallel functional networks derived from surface electromyography (sEMG) and electroencephalography (EEG) signals.</p> Methods <p>Sixteen healthy participants performed sustained isometric elbow flexion tasks under non-fatigued and fatigued conditions. Multichannel sEMG signals from eight upper limb muscles and EEG signals from 21 scalp locations were simultaneously recorded. Functional muscle networks and functional brain networks were constructed independently using generalized partial directed coherence. Network topology was quantified using average clustering coefficient (ACC), average global efficiency (AGE), and average shortest path length (APL) in relevant frequency bands.</p> Results <p>In the muscle functional network, fatigue was associated with a significant increase in ACC and AGE, accompanied by a reduction in APL, indicating enhanced local clustering and more efficient information transfer among muscles. In the brain functional network, significant changes were observed primarily in the beta-band, with increased ACC and AGE and decreased APL following fatigue. In contrast, gamma-band network metrics showed limited or non-significant alterations. These results suggest that fatigue-related neuromuscular adaptation is reflected at the network topology level rather than through isolated signal features.</p> Conclusion <p>The proposed brain and muscle functional network framework provides a system-level characterization of upper limb muscle fatigue based on parallel EEG and sEMG network analysis. By capturing fatigue-related changes in network topology, this approach provides a system-level reference for investigating neuromuscular coordination under fatigue and lays a methodological foundation for future rehabilitation-oriented fatigue monitoring studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2026年3月18日

查看全文
  • Xiaoguang Liu,
  • Pengyuan Lin,
  • Aoqi Guo,
  • Tie Liang,
  • Jun Li,
  • Zhaopeng Li

摘要

Purpose

Muscle fatigue is accompanied by coordinated changes in both neuromuscular activation and cortical activity. Traditional fatigue assessment methods based on single physiological signals often fail to capture system-level reorganization of the brain–muscle control network. This study aims to characterize upper limb muscle fatigue by analyzing parallel functional networks derived from surface electromyography (sEMG) and electroencephalography (EEG) signals.

Methods

Sixteen healthy participants performed sustained isometric elbow flexion tasks under non-fatigued and fatigued conditions. Multichannel sEMG signals from eight upper limb muscles and EEG signals from 21 scalp locations were simultaneously recorded. Functional muscle networks and functional brain networks were constructed independently using generalized partial directed coherence. Network topology was quantified using average clustering coefficient (ACC), average global efficiency (AGE), and average shortest path length (APL) in relevant frequency bands.

Results

In the muscle functional network, fatigue was associated with a significant increase in ACC and AGE, accompanied by a reduction in APL, indicating enhanced local clustering and more efficient information transfer among muscles. In the brain functional network, significant changes were observed primarily in the beta-band, with increased ACC and AGE and decreased APL following fatigue. In contrast, gamma-band network metrics showed limited or non-significant alterations. These results suggest that fatigue-related neuromuscular adaptation is reflected at the network topology level rather than through isolated signal features.

Conclusion

The proposed brain and muscle functional network framework provides a system-level characterization of upper limb muscle fatigue based on parallel EEG and sEMG network analysis. By capturing fatigue-related changes in network topology, this approach provides a system-level reference for investigating neuromuscular coordination under fatigue and lays a methodological foundation for future rehabilitation-oriented fatigue monitoring studies.