Delineating the Role of Alpha Waves in Exercise-induced Neural Changes through Resting-state EEG

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Abstract
Exercise intervention demonstrates unique potential in treating emotional dysregulation, yet the ambiguity of its neuromodulation targets hinders the development of precise exercise prescriptions. This study investigates trait anxiety as a representative emotional disorder in 40 high-trait-anxiety university students, who were randomly assigned to either an exercise intervention group (40 min moderate-intensity aerobic exercise, n = 20) or a non-exercise control group (40 min quiet reading, n = 20), followed by resting EEG data collection. By integrating resting-state electroencephalography (EEG) after exercise with deep learning algorithms, we developed an alpha-band time-frequency predictive model to systematically decode the neural oscillatory reprogramming mechanisms in the prefrontal cortex induced by exercise. The deep learning model exhibited superior classification efficacy (accuracy 83.33%, F1 score 0.83, Kappa coefficient 0.67) in identifying exercise-induced alpha-band power spectral entropy alterations. This study pioneers the identification of prefrontal Alpha excitatory rebalancing through neural oscillation remodeling as the core mechanism underlying exercise-mediated anxiety mitigation.

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