Work

Temporal Attention and Convolutional Tokenization for Interpretable EEG-Based ADHD Identification in Children

Year: 2026

Type: article

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition commonly assessed through clinical interviews, behavioral observation, and rating scales. Although electroencephalography (EEG) has emerged as a promising complementary tool for ADHD assessment, robust subject-independent classification remains challenging due to inter-subject variability, limited datasets, and the need for interpretable computational models. This work introduces EEG-TACT, a compact end-to-end deep learning architecture for identifying ADHD subjects from EEG epochs. The proposed model integrates an EEGNet-inspired convolutional embedding, a Transformer encoder operator, and an attention-based pooling mechanism to jointly capture local spatiotemporal EEG patterns, contextual temporal dependencies, and task-relevant latent representations. EEG-TACT was evaluated on a publicly available EEG dataset using strict, subject-independent stratified group partitions, ensuring that data from the same subject were never shared across the training, validation, and test subsets. Model interpretability is examined through learned temporal filter responses, class-conditioned self-attention maps, and latent-space projections, while an ablation study quantifies the contribution of each architectural component. Performance was assessed at the fold, subject, and epoch levels, together with statistical significance comparisons against representative state-of-the-art architectures. EEG-TACT outperformed the contrasted models, achieving subject-level accuracy of 87.5%, recall of 96.0%, and precision of 82.8%, while requiring only a few thousand trainable parameters. By exhaustively repeating the initialization, the proposed model demonstrated improved labeling reliability and achieved the best average ranking among the evaluated architectures. The reported results therefore prove that EEG-TACT provides a compact, robust, and interpretable model for EEG-based ADHD identification under subject-independent evaluation settings.

Source: Technologies

DOI: 10.3390/technologies14070392

Authors: Julián David Pastrana-Cortés, Alejandra Gomez-Rivera, Andres Marino Álvarez-Meza, Julian Gil-Gonzalez, David Cárdenas-Peña

Institutions: Technological University of Pereira, Universidad Nacional de Colombia

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: $1,930