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

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.