EEG-Based Diagnosis of ADHD in Children and Adolescents: A Comprehensive Review of Machine Learning and Deep Learning Approaches

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a major challenge in pediatric neurodevelopment and one of the most common conditions in children and adolescents. Clinical diagnosis remains difficult due to symptom overlap with other disorders, increasing the need for objective tools. Electroencephalography (EEG) has emerged as a non-invasive technique that, when combined with artificial intelligence, enables more reliable diagnostic models. This study aims to review pediatric and Adolescent EEG-based ADHD studies published between 2015 and 2025, covering both traditional signal-processing and artificial intelligence approaches.Fifty studies were included in this review study.The  originality of this review lies in providing a decade-long, child-focused synthesis that highlights accuracy trends, methodological progress, and challenges related to clinical translation. Reported performance varied widely: traditional spectral analyses with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) achieved 62–88%, whereas deep learning models including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and attention-based hybrids,often exceeded 99%. The limitations across the literature include small sample sizes, restricted multi-center datasets, and insufficient evaluation of ADHD subtypes.