Objective: Event-related EEG activity is widely investigated to characterize brain functions. Traditional analyses rely on heavy pre-processing and strong a priori assumptions, which limit reproducibility and may obscure task-relevant neural activity. This study aims to validate an interpretable convolutional neural network (CNN) capable of highlighting frequency-, spatial-, and temporal-domain EEG signatures in an automatic, data-driven, and end-to-end manner. Methods: We simulated single-trial EEG with imposed spatio-temporal or spectral-spatio-temporal modulations in two paradigms: a visual oddball task and a motor task (200 participants and 200k trials overall). An interpretable CNN was applied to each cognitive task at the single-participant level. CNN-derived spectral, spatial, and temporal signatures were compared with ground-truth signatures known from the simulation by computing localization errors and accuracies. Results: Network features reproduced the modulations imposed in the simulations. The network localized neural signatures with high accuracy: average spectral, spatial, and temporal localization accuracies reached up to 85.3%, 97.8%, and 97.1% across tasks, respectively (top-1 prediction). The corresponding average localization errors were well within established EEG resolution limits (down to 0.95 Hz spectral, 5.7 mm spatial, and 30 ms temporal errors). Conclusion: The interpretable CNN accurately recovered task-relevant EEG signatures across domains, thereby supporting the validity of a CNN-based EEG analysis. Significance: This study provides a ground-truth-based quantitative validation of the multi-domain features learned in interpretable CNNs for EEG analysis, establishing a meaningful reference for trustworthy deep-learning tools that can enhance participant-specific EEG interpretation. These individualized tools could enhance our comprehension of brain functions in both healthy participants and patients.
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