A. Álvarez-Meza

Transformer-Based Modeling of Directed Transfer Entropy Connectivity for EEG-Based ADHD Classification in Children

The Contextualized Transfer Entropy Network (CTE-Net) is proposed, an end-to-end deep-learning architecture that combines global content-based contextualization with nonlinear and directed EEG connectivity estimation and exhibited the lowest within-subject dispersion among the analyzed representation stages.