Analyzing Frequency-Space-Time EEG Signatures via Interpretable Neural Networks: A Simulation Study

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.

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.

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).

The interpretable CNN accurately recovered task-relevant EEG signatures across domains, thereby supporting the validity of a CNN-based EEG analysis.

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.