Predicting individual muscle fatigue tolerance by resting-state EEG brain network ^*

Abstract

Objective: Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored. Approach: Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e., elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance. Main Results: Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another thirty-subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network. Significance: Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.

Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

“…Subjects in these studies are recruited based on the history of their neurological disorder [21]- [44] diabetes [45], [46], obesity [46], cardiovascular disease [25], [27], [30], [34], [42], [45]- [47], muscular issues [44], [48], and sleep disorder [49]. People who are taking medications [21], [22], [32], [43], [45], [46], [50]- [52], smoking [21], [39], [45], [49], [51], alcohol, tea, coffee [22], [25], [35], [37], [38], [45], [52], [53] are not considered. People who had done strenuous exercise before the experiment are avoided [22], [37], [38], [44], [52].…”

“…People who are taking medications [21], [22], [32], [43], [45], [46], [50]- [52], smoking [21], [39], [45], [49], [51], alcohol, tea, coffee [22], [25], [35], [37], [38], [45], [52], [53] are not considered. People who had done strenuous exercise before the experiment are avoided [22], [37], [38], [44], [52].…”

The potential of fNIRS, EEG, and transcranial current stimulation to probe neural mechanisms of resistance training

“…Previous EEG and fNIRS studies suggest that the prefrontal cortex (PFC) plays a critical role in the regulation of cortical motor drive during eccentric movement, particularly when compared to isometric and concentric contractions (Perrey, 2018 ; Borot et al, 2023 ). After performing biceps curl exercises until muscle failure, Li et al ( 2022 ) observed a decrease in the efficiency of brain networks in the beta frequency band associated with motor control processes. This suggests that acute bouts of strength exercise modulate the functional characteristics of electrophysiological brain networks in an intensity-dependent manner.…”

Review of Neural Networks in the EEG Signal Recognition

“…Most often, fatigue detection through EEG signals relies on analyzing the alpha rhythm. The alpha rhythm has consistently demonstrated a strong correlation between its signal strength and the subject's level of fatigue [11,6]. Researchers commonly utilize the signal power spectral density to investigate this correlation.…”