An Interpretability Framework for Convolutional Neural Network-Based Electroencephalography Analysis Discovers New...

Deep learning (DL) models, particularly convolutional neural networks (CNNs), have shown promise in automated epileptic seizure detection from electroencephalogram (EEG). However, their "black-box" nature limits clinical adoption, as interpretability is critical for trust and validation in medical applications. A novel interpretability method for CNN-based seizure detection models, designed to uncover meaningful spatial and spectral EEG biomarkers, is proposed. The approach combines frequency- and spatial-domain interpretation to provide both global model behavior analysis and local, sample-specific explanations. It also accounts for the task-specific design, neurophysiological grounding and cross-framework validation - concepts often neglected by many state-of-the-art methods. Results are represented as heatmap matrices of feature importance (5 frequency bands * 5 brain regions) with important features determined through statistical testing. Interpretation is based on neurophysiological alignment of these features. The method is validated on three CNN architectures, demonstrating how each leverages distinct frequency bands and brain regions for seizure identification. Global interpretation reveals that the highest-performing model utilizes complementary biomarkers across multiple frequency bands, while local interpretation captures dynamic intra-seizure spectral shifts. The results align with known neurophysiological mechanisms, such as thalamocortical interactions (theta-band) and default mode network suppression (alpha/beta-bands), while also suggesting new biomarkers for seizure detection. The method bridges the gap between DL and clinical EEG analysis, offering a tool for model validation and discovery of electrophysiological signatures in epilepsy.