EpilepsyNet-XAI: Towards High-Performance and Explainable Multi-Phase Seizure Analysis from EEG Features

Epilepsy is a long-term neurological disorder affecting more than 65 million people worldwide, and accurate detection of its phases from electroencephalogram (EEG) signals is essential for diagnosis and patient management. This paper presents a comprehensive method for multi-phase seizure classification using robust feature engineering, advanced machine learning (ML), deep learning (DL), and explainable artificial intelligence (XAI). A rich set of EEG features is constructed, combining traditional and specialized metrics to capture subtle neurophysiological shifts across seizure phases. Exploratory data analysis demonstrates the discriminative power of these features, with PCA and t-SNE revealing distinct non-linear clusters. Multiple ML models—including Random Forest, Support Vector Machines, K-Nearest Neighbors, LightGBM, and XGBoost—are evaluated using 5-fold stratified cross-validation, achieving consistently high performance. The proposed MLP-based EpilepsyNet-XAI model outperforms all baselines. Post hoc XAI techniques such as LIME are applied to enhance transparency and interpretability in the classification process. By integrating high-performing models with interpretable analysis, this work supports more reliable AI-driven approaches for methodological epilepsy research and analysis.