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Attention deficit hyperactivity disorder (ADHD) is a profound neurodevelopmental disorder. Currently, the diagnosis of ADHD relies on clinical assessments and lacks objective testing. Research in electroencephalography (EEG) offers new hope for the diagnosis of ADHD, with researchers actively seeking objective EEG biomarkers. This study conducts a bibliometric analysis of the application of EEG in ADHD, aiming to provide a brief overview of the characteristics, main research areas, development paths, and trends in this field. The Web of Science Core Collection was queried on June 10, 2024, to gather relevant scholarly works from the period of 2004 to 2023. Analysis was conducted using CiteSpace, VOSviewer, and Microsoft Excel 2019. In the past 20 years, 1162 documents qualified, with a swift rise in annual publications. The USA, University of London, and Barry RJ led in productivity and impact, while the Clinical Neurophysiology topped in publication volume and citations. High-frequency terms include "ADHD," "EEG," "event-related potentials (ERP)," "children," and "neurofeedback." Clustering key terms such as "cognitive control," "theta waves," "epilepsy," "graph theory," "machine learning," and "neurofeedback" form the cornerstone of the current core research areas. At the same time, a series of emerging research frontiers are gradually emerging, including "theta/beta ratio (TBR)," "P300 wave," "neurofeedback," and "deep learning." Over the past 2 decades, research on the application of EEG in ADHD has been burgeoning, with themes becoming increasingly profound. These insights provide key guidance on current trends, development trajectories, and future challenges in the field.
Liu et al. (Fri,) studied this question.