Explainable machine-learning integration of pre-task and preparatory EEG: revealing complementary predictive information for expert action reliability.

Introduction: Expert motor behavior varies among athletes and across repetitive actions; however, conventional brain-behavior analyses can conflate these statistical levels. We characterized the behavioral architecture of expert table tennis serves and tested whether pre-task and action-preparatory EEG contribute information at different behavioral scales. Methods: A total of 22 expert athletes completed 100 cue-directed serves with trial-resolved landing coordinates. Unsupervised principal component analysis characterized complete landing distributions. Eyes-closed pre-task EEG provided a hypothesis-informed alpha-organization index combining individual alpha peak frequency and fronto-central alpha coherence. EEG spectral power during action preparation was evaluated using nested leave-one-athlete-out learning, within-athlete permutation testing, grouped permutation importance, and multiscale probability integration. Results: The two dominant landing components explained 57.2% of behavioral variance and primarily reflected reproducible individualized placement patterns, whereas a smaller component (7.4% explained variance) was more closely aligned with task-defined performance. Pre-task alpha organization was associated with higher target-zone reliability ( Discussion: Pre-task and preparatory EEG carried complementary rather than interchangeable predictive information. Pre-task EEG was related to between-athlete performance propensity, whereas preparatory EEG offered weak but reliable within-athlete trial evidence. The value of the transparent machine-learning framework did not lie solely in achieving high prediction accuracy; rather, it was its capability to discover behavioral structure, test cross-athlete generalization, explain predictive information, and integrate neural evidence at a statistical level relevant to its operation.