Resolution-aware spectral parameterization reveals robust context-dependent aperiodic EEG dynamics during real-world table tennis

Abstract

Mobile EEG can link neuroimaging to natural behavior, but effects recorded during movement, and sport in particular, are difficult to interpret because neural activity overlaps with signals from movement, muscle activity, and cable movement artifacts. We analyzed the OpenNeuro Real World Table Tennis dataset to test whether peri-hit spectral activity could be analyzed and how it differed between human-opponent and ball-machine play. Data from 25 participants comprising EEG, noise-layers, EMG and motion recordings was analyzed with pre-hit, impact, and post-hit windows. EEG power bands were residualized using trial-level root mean square from noise-layer, EMG, and motion/miscellaneous channels. Spectral parameterization modeled spectra as an aperiodic component plus periodic peaks. The principal short-window model was resolution-aware; sensitivity analyses reduced model complexity, used a 2.0-s peri-hit subwindow (-1.0 to +1.0 s), or disabled periodic fitting. Results showed residualized alpha and beta power was lower during ball-machine play, including pre-hit posterior alpha power. The clearest context difference was a higher ball-machine aperiodic exponent over posterior and parietal sensors, corresponding to a steeper broadband spectral slope. Posterior post-hit means were 0.451 and 0.606 for human-opponent and ball-machine play, respectively. A focal parietal effect persisted across specifications. The results support artifact-informed, resolution-aware spectral analysis as an effective approach for detecting robust spectral differences between naturalistic visuomotor contexts and suggest that EEG may provide useful information about the processes engaged by different practice environments, without establishing a single cognitive or physiological mechanism or demonstrating a training outcome.