Objective: The aim of this study was to examine how transcranial electrical stimulation (tES) modulates intermuscular coherence (IMC) in sprinters and develop an interpretable neural network model for performance prediction. Methods: Thirty elite sprinters completed a randomized crossover trial involving three tES conditions: motor cortex stimulation (C1/C2), prefrontal stimulation (F3), and sham. Sprint performance metrics (0-100 m phase analysis) and lower-limb sEMG signals were collected. A Kolmogorov-Arnold Network (KAN) was trained to decode neuromuscular coordination-sprint performance relationships using IMC and time-frequency sEMG features. Results: Motor cortex tDCS increased 30-60 m sprint velocity by 2.2% versus sham (p < 0.05, ?2 = 0.25). ?-band IMC in key muscle pairs (rectus femoris-biceps femoris, tibialis anterior-gastrocnemius) significantly heightened under motor cortex stimulation (F > 4.2, p < 0.03). The KAN model achieved high predictive accuracy (R2 = 0.83) through cross-validation, with derived symbolic equations mapping neuromuscular features to performance. Conclusions: Targeted tDCS enhances neuromuscular coordination and sprint velocity, while KAN provides a transparent framework for performance modeling in elite sports.
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