ArticleAnnals of biomedical engineering2026
Upper Limb Fatigue Information Variation Analysis Based on Parallel Brain and Muscle Functional Networks.
Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- An Upper Limb Muscle Fatigue Detection Approach for Overhead Work Using Interface Pressure Complexity Measurement.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
purposeMuscle 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.
methodsSixteen 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.
resultsIn 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.
conclusionThe 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.
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