Explainable Convolutional Channel Ranking (ECCR) for EEG-Based Detection of Idiopathic Absence Seizures

Accurate identification of EEG electrodes associated with epilepsy is essential for developing real-time diagnostic applications. This paper introduces the Explainable Convolutional Channel Ranking (ECCR) method for identifying diagnostically relevant EEG channels for Idiopathic Absence Seizure (IAS) detection by analyzing channel-specific feature contributions learned by a convolutional neural network (CNN). Unlike traditional saliency-based approaches that focus only on highly activated regions or pool contributions across seizure types and spatial locations, ECCR retains channel-specific contribution patterns and shows that channels with moderate contribution levels offer the most discriminative and physiologically consistent information. This finding suggests that channels with very high saliency are often affected by noise or subject-specific artifacts, while medium-contribution channels capture more stable seizure-related information dynamics. In 10-fold cross-validation, the ECCR-guided CNN achieved 82.21% accuracy and 92.01% sensitivity, while leave-one-subject-out (LOSO) validation yielded 73.78% accuracy, demonstrating improved subject-independent performance under a leakage-controlled protocol; ECCR consistently selected fronto-central, temporo-parietal, and occipital regions, reducing 29 channels to 7 in the subject-dependent evaluation. A validation using a Random Forest classifier confirmed that ECCR-selected channels provided stronger detection power than those excluded. These findings suggest that ECCR can guide the design of compact, interpretable EEG systems, supporting more reliable deep learning solutions for IAS diagnosis.