EEG-based ADHD diagnosis models suffer from two persistent issues: data leakage and the lack of physiologically grounded interpretability, limiting clinical adoption. To address these, this paper presents 3D ADHD-Net and DeepTrace. Unlike established 1D and 2D studies, 3D ADHD-Net is a topology-aware spatiotemporal model that preserves scalp geometry by projecting raw EEG onto a grid, preventing spatial information loss. DeepTrace is a novel explainability framework that traces diagnostic…
