Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Mathematics

DOI: 10.3390/math14010125

Authors: Sajid Ur Rehman, Faisal Mehmood, Young-Jin Kim, Hachul Jung

Institutions: Air University, Gachon University, Osong Medical Innovation Foundation

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137