Explainable ai-based connectivity learning for ADHD detection from EEG signals

Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) remains difficult to assess objectively because diagnosis still depends largely on behavioral evaluation, while EEG-based computational approaches often face a trade-off between subject-independent robustness and physiological interpretability. To address this limitation, this work proposes an EEG-based framework that integrates Transformer-based temporal modeling with multi-scale Gaussian kernel connectivity regularized through $alpha$-Rényi mutual information, while incorporating Graph Spectral Analysis with XGBoost from Phase Locking Value-based connectivity to examine the class-discriminative structure of the learned representations. The approach was evaluated on a publicly available pediatric EEG dataset comprising 121 children under a 5-fold Stratified Group k-Fold cross-validation protocol designed to preserve subject independence. The experimental results show competitive performance, with 80.44% accuracy, 80.37% recall, and 81.35% precision, surpassing EEGNet, Multi-Stream Transformer, IM-CBGT, and ANOVA-PCA SVM baselines; additionally, the analysis reveals that the learned connectivity patterns become progressively more discriminative from raw EEG to higher-level transformed representations. Overall, these findings support the potential of the proposed framework as a robust and interpretable alternative for objective EEG-based ADHD assessment.