EEG-Based Brain Dynamics in Motor Execution and Imagery: Spectral Analysis with Potential Applications in Neurorehabilitation
Audrey Rah, Yuhua ChenThis offline study examines alpha and beta spectral differences around the task annotations of the PhysioNet Electroencephalography (EEG) Motor Movement/Imagery Dataset. Motor-cortex EEG was analyzed from the PhysioNet motor-execution and motor-imagery recordings. The time origin was the dataset task annotation (T1 or T2), which marks the instructed target interval rather than independently measured movement onset. The primary one-second window, from −1.0 to 0.0 s, precedes the task annotation and lies in the preceding rest block when that block fully contains it. Spectral power features in the alpha and beta frequency bands were computed using Welch’s method. Subject-wise z-score normalization was applied prior to statistical analysis. A comprehensive statistical evaluation was performed, including non-parametric group comparisons, pairwise post hoc tests with Bonferroni correction, effect size estimation, bootstrap confidence intervals, normality assessment, and rank correlation analysis. The original 109-subject Kruskal–Wallis contrast is significant (Hα=74.79, p≪0.001; Hβ=289.01, p≪0.001). Reanalysis established that these alpha and beta power differences compare the end of rest with the end of the preceding task, rather than a measured pre-movement interval with rest. Under the Methods-faithful per-trial ERD/ERS definition, alpha ERD did not differ significantly across rest/left/right (H=3.95, p=0.139) and is therefore not treated as a primary confirmed finding. Supplementary subject-held-out classification (107 participants) yielded an AUC of 0.595 for post-cue versus pre-cue rest discrimination, while pre-cue prediction of the upcoming hand remained at chance after sequence control. These findings do not establish limb-specific intention detection before movement. These findings characterize sensorimotor EEG dynamics and provide a foundation for future research on EEG-informed neurorehabilitation.