Leakage-Aware Decoding of Music Perception and Cued Imagery Across the Full OpenMIIR EEG Cohort: A Reproducible Analysis of the Generalization Boundary

Abstract

Music perception and musical imagery provide a controlled setting for studying whether scalp electroencephalography (EEG) captures reproducible differences between externally driven and internally generated auditory states. We tested whether the public OpenMIIR dataset supports leakage-aware decoding of music perception versus cued musical imagery across its full ten-subject cohort, and we characterized the boundary beyond which the decoded signal fails to generalize. Using compact spectral and temporal EEG features, we applied stratified trial-grouped cross-validation, dummy and shuffled-label negative controls, leave-one-subject-out (LOSO) testing, a 1000-fold trial-level label-permutation test, and a group-level one-sided Wilcoxon test over per-subject within-subject accuracies, with Benjamini-Hochberg (BH) correction across the family of tested hypotheses. Within subjects, decoding was above chance at the population level: a group Wilcoxon test on logistic-regression accuracy gave p = 0.0195 with a large effect size (Cohen's dz = 0.95; 7 of 10 subjects above chance), confirmed by a pooled trial-level permutation test (p = 0.0040). Pooled trial-grouped balanced accuracy reached 0.567 [0.550, 0.586] for random forest and 0.543 [0.523, 0.561] for logistic regression, exceeding both dummy and shuffled-label controls. Cross-subject transfer was weaker and model-dependent: under LOSO, random forest reached 0.559 [0.523, 0.597], above its dummy baseline (uncorrected p = 0.014), whereas logistic regression did not generalize (0.518, p = 0.165). Under BH correction across the nine tested hypotheses, six comparisons survived at q < 0.05 (smallest q = 0.036), all involving the permutation test or the nonlinear model, while the linear model's cross-subject contrasts did not. These results indicate that OpenMIIR EEG supports modest but reproducible within-subject discrimination of music perception and cued imagery, with a linear-within-subject versus nonlinear-cross-subject generalization boundary, and they show why public EEG music data require leakage-aware validation and calibrated subject-generalization claims.