ADHD detection from EEG signals using GCN based on multi-domain features.

Introduction: Attention deficit hyperactivity disorder (ADHD) is a common psychiatric disorder in children during their early school years. While many researchers have explored automated ADHD detection methods, developing accurate, rapid, and reliable approaches remains challenging. Methods: This study proposes a graph convolutional neural network (GCN)-based ADHD detection framework utilizing multi-domain electroencephalogram (EEG) features. First, time-domain and frequency-domain features are extracted via long short-term memory (LSTM) and convolutional neural network (CNN) models, respectively. Second, a novel functional connectivity matrix is constructed by fusing phase lag index (PLI) and coherence (COH) features to simultaneously capture phase synchrony and signal intensity consistency between brain regions. Finally, a GCN model integrates these time-frequency features with topological patterns from the connectivity matrix for ADHD classification. Results: Evaluated on two EEG datasets, the proposed method achieved average accuracies of 97.29% and 96.67%, outperforming comparative models (XGBoost, LightGBM, AdaBoost, random forest). Visualization experiments further revealed distinct brain connectivity distributions between ADHD patients and healthy controls. Discussion: The fused functional connectivity matrix surpasses traditional single-metric approaches in characterizing brain interactions. By synergistically combining time, frequency, and topological features, the GCN framework enables more precise ADHD detection. This method demonstrates potential for assisting neurologists in clinical diagnosis while providing interpretable neurophysiological insights.