
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312386
MD-D-24-08574
00087
10.1097/MD.0000000000039668
3
6200
Research Article
Observational Study
Global research progress of electroencephalography applications in attention deficit hyperactivity disorder: Bibliometrics and visualized analysis
Liu Ben BD liuben@ycsyy.com
a
Liu Xian MD liuxian@ycsyy.com
b
Wei Jie BD weijie@ycsyy.com
a
Sun Siyuan BD sunsiyuan@ycsyy.com
a
Chen Wei BD chenwei2@ycsyy.com
a
https://orcid.org/0009-0001-2527-9437
Deng Yijun PhD c*
a Pediatric Intensive Care Unit, Yancheng No. 1 People’s Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
b Department of Pediatrics, Yancheng No. 1 People’s Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
c Yancheng No. 1 People’s Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China.
* Correspondence: Yijun Deng, President’s Office, Yancheng No. 1 People’s Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng 224000, China (e-mail: dengyijun001@126.com).
20 9 2024
20 9 2024
103 38 e3966827 7 2024
21 8 2024
22 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

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.

ADHD
attention deficit hyperactivity disorder
developing trends
EEG
electroencephalography
visual analysis
OPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

Attention deficit hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder, affects about 5% to 7% of children globally.[1,2] Symptoms like inattention, hyperactivity, and impulsivity significantly impact children’s behavior and school/home performance.[3] Diagnosing ADHD, while adhering to the “Diagnostic and Statistical Manual of Mental Disorders (DSM-5™)” guidelines, is complex due to its reliance on subjective reports from parents, teachers, and physician assessments.[4] Concerns arise over the accuracy of such diagnoses.[2] The growing interest in neurophysiological data is aimed at refining ADHD diagnosis, as neurophysiological studies reveal distinct electroencephalography (EEG) patterns in ADHD patients that underscore their unique neuropsychological profiles.[5,6] EEG technology stands out among many biometric technologies due to its universality, uniqueness, cost-effectiveness, and ease of operation.[7] As a result, an increasing number of scholars are paying attention to and researching the application of EEG in ADHD patients, hoping to provide new perspectives and methods for diagnosis and treatment in this field.[8]

Despite a surge in EEG studies for ADHD, tracking their overall progress remains challenging. Bibliometrics, a quantitative analysis tool, assesses the productivity, structure, and impact of this research.[9] Unlike traditional reviews, bibliometrics visually maps knowledge trends and landscapes, offering a broad view of research fields.[10] No specific bibliometric analysis has yet addressed EEG’s role in ADHD. This study aims to bridge this gap, using bibliometric methods to explore key features, trends, and future paths, contributing to the field’s advancement.

2. Materials and methods

2.1. Data collection

The research accessed data via the Science Citation Index Expanded, part of the Web of Science Core Collection (WoSCC). The search query was conducted on June 10, 2024. The study focused on “ADHD” and “EEG” as the main keywords, with additional synonyms and related terms included for comprehensive article selection.[11] A Boolean search formula was used to find all “ADHD” and “EEG” related documents in titles, abstracts, and keywords on Web of Science. Details are in Supplement 1, http://links.lww.com/MD/N570. The study included English articles and reviews from 2004 to 2023. Two researchers, W.J. and S.S.Y., excluded irrelevant literature, with disputes resolved by D.Y.J. and L.X. A total of 1162 articles were identified (Fig. 1).

Figure 1. The flow diagram of literature enrollment and data screening.

2.2. Data analysis

The bibliographic dataset was uploaded to VOSviewer 1.6.19 and CiteSpace 6.3.R3 for comprehensive analysis.[12] CiteSpace, a Java app by Chen C, visualizes scientific knowledge domains with algorithms and tools. Nodes represent entities like countries, institutions, authors, etc. Node connections show collaborations, with thicker lines for stronger ties. The outer purple ring shows centrality levels, with high centrality (>0.1) indicating significant network influence. Red marks on nodes signal recent citation bursts.[13] VOSviewer is a scientific literature visualization tool that helps users analyze patterns and trends in literature through network graphs, identifying key participants, collaboration networks, thematic developments, and citation patterns.[14] The publication volume for each year was presented using Microsoft Excel 2019.

3. Results

3.1. Analysis of publication trends

Between 2004 and 2023, 1162 papers met the criteria, showing an overall rise in publication numbers with occasional declines (Fig. 2). Notably, 2022 saw a peak with 133 papers, and the recent 5 years accounted for 47.07% of the total.

Figure 2. Annual output and citation times of publications with EEG applications in ADHD from 2004 to 2023. ADHD = attention deficit hyperactivity disorder, EEG = electroencephalography.

3.2. Analysis of countries/regions and institutions

Analyzing publications from 69 nations and 1751 institutions, the US tops with 282 (24.27%), followed by Germany (186, 16%), China (135, 11.62%), the UK (121, 10.41%), and Australia (110, 9.47%) (Table 1). Figure 3A shows the US (0.29), Germany (0.14), and the UK (0.13) leading in centrality. However, Norway (0.21), India (0.13), Saudi Arabia (0.15), and Pakistan (0.1) have notable centrality but aren’t in the top 10 for publication count. Additionally, China, Norway, and Denmark also saw citation surges in their academic works.

Table 1 Ranking of the top 10 countries/regions and institutions that have published the most articles from 2004 to 2023.

Rank	Country	Count	Centrality	Institution	Count	Centrality	
1	USA	282 (24.27%)	0.29	University of London (UK)	68	0.3	
2	Germany	186 (16.00%)	0.14	University of California System (USA)	58	0.12	
3	China	135 (11.62%)	0.09	University of Wollongong (Australia)	48	0.04	
4	UK	121 (10.41%)	0.13	University of Zurich (Switzerland)	35	0.04	
5	Australia	110 (9.47%)	0.1	Ruprecht Karls University Heidelberg (Germany)	34	0.02	
6	Netherlands	83 (7.14%)	0.02	Central Institute of Mental Health (Germany)	32	0.02	
7	Switzerland	77 (6.63%)	0.09	Eberhard Karls University of Tubingen (Germany)	27	0.06	
8	Canada	60 (5.16%)	0.06	Harvard University (USA)	26	0.12	
9	Italy	59 (5.08%)	0.07	Zurich Center Integrative Human Physiology (Switzerland)	23	0	
10	Iran	54 (4.65%)	0.02	Technische Universitat Dresden (Germany)	19	0.02	

Figure 3. (A) Cooperation network among countries with EEG applications in ADHD from 2004 to 2023. (B) Visualization map of institutions’ cooperative relations in the field of EEG applications in ADHD (2004–2023). ADHD = attention deficit hyperactivity disorder, EEG = electroencephalography.

Table 1 highlights the top research-producing institutions. The University of London tops with 68 papers, followed by the University of California System (58), University of Wollongong (48), University of Zurich (35), and Heidelberg University (34). Germany is notably represented with 4 institutions in the top ten. Figure 3B illustrates the institutional collaboration network, with the University of London (0.3), University of California System (0.12), and Harvard University (0.12) showing high centrality. Institutions like the University of Wollongong and ZIHP also see significant citation increases.

3.3. Analysis of authors

A total of 4873 authors have contributed to this research field, with Barry RJ and Clarke AR, and McCarthy R and Selikowitz M ranking top with 44 and 33 papers, respectively (Table 2). Our team’s multiple data reviews confirmed these findings. Arns M leads in total citations at 1863, followed by Barry RJ and Clarke AR with 1782. Clarke AR tops co-citations with 1125, highlighting his influence in EEG and ADHD research. Figure 4A shows key academic teams’ growth, with notable collaborations like Barry RJ and Clarke AR. Figure 4B and C reveal inter-cluster cooperation, such as Johnstone SJ and Arns M, and emerging teams like Sun L’s, as shown in Figure 4D.

Table 2 Ranking of the top 10 most productive authors and most co-cited authors from 2004 to 2023.

Rank	Author	Count	Citations	Total link strength	Co-cited author	Citations	Total link strength	
1	Barry RJ	44	1782	147	Clarke AR	1125	37,221	
2	Clarke AR	44	1782	147	Barry RJ	708	21,782	
3	Mccarthy R	34	1332	131	Arns M	420	12,327	
4	Selikowitz M	34	1332	131	Loo Sandra	392	14,027	
5	Brandeis D	32	1506	92	Barkley RA	274	8228	
6	Loo Sandra	32	1706	90	Monastra Vince	270	8883	
7	Johnstone SJ	25	1030	91	Chabot RJ	267	9662	
8	Arns M	23	1863	53	Castellanos FX	213	6849	
9	Mcloughlin G	23	705	85	Klimesch W	208	6781	
10	Asherson P	23	613	92	Faraone SV	205	6388	

Figure 4. (A) Collaborative network map among authors in the field of EEG applications in ADHD (2004–2023); (B and C) Inter-cluster cooperation network between academic teams; (D) Temporal view of author collaboration network. ADHD = attention deficit hyperactivity disorder, EEG = electroencephalography.

3.4. Analysis of journals

Academic journals play a crucial role in disseminating the findings of scientific research. Among the 1162 publications analyzed, 357 academic journals were identified. The top 10, publishing 405 articles or 25.73% of the total, mainly focus on neurophysiology and psychophysiology (Table 3). The Clinical Neurophysiology led with 60 articles, followed by Clinical EEG and Neuroscience at 53 and International Journal of Psychophysiology at 35. The most cited were the Clinical Neurophysiology (2518), Neuroscience and Biobehavioral Reviews (2503), and Clinical EEG and Neuroscience (1823). Additionally, among the top 10 journals, 5 are from the United States; and among the top 10, only 2 have an Impact Factor greater than 3.0.

Table 3 Ranking of the top 10 most productive journals from 2004 to 2023.

Rank	Journal	Article count	Country/region	Journal Citation Reports (2024)	Impact factor (2024)	Total number of citations	Total link strength	
1	Clinical Neurophysiology	60	Ireland	Q1	3.7	2518	747	
2	Clinical EEG and Neuroscience	53	USA	Q3	1.6	1823	489	
3	International Journal of Psychophysiology	35	Netherlands	Q2	2.5	1296	414	
4	Frontiers In Human Neuroscience	29	Switzerland	Q2	2.4	1281	293	
5	Epilepsy & Behavior	25	USA	Q2	2.3	330	48	
6	PLoS One	22	USA	Q1	2.9	657	105	
7	Frontiers In Psychiatry	20	USA	Q2	3.2	233	146	
8	Brain Sciences	19	Switzerland	Q3	2.7	102	99	
9	Biological Psychology	18	Netherlands	Q1	2.7	857	223	
10	Journal of Attention Disorders	18	USA	Q2	2.7	821	303	

3.5. Analysis of keyword

Keywords reveal the core themes and focal points of academic articles. In the network graph of Figure 5A, node size indicates keyword frequency, and color depth reflects the time period of keyword co-occurrence. The graph shows that “machine learning,” “deep learning,” “biomarkers,” “classification,” and “neurodevelopmental disorders” are frequently co-occurring keywords in recent times. The top 5 most frequent keywords are “ADHD” (689), “EEG” (546), “ERP” (104), “children” (89), and “neurofeedback” (89) (Table 4). A time-zone co-occurrence analysis (Fig. 5B) was then performed, illustrating the progression of research trends and focal points. In the analysis, keyword cluster analysis, using Citespace’s LLR algorithm, revealed significant structures among author keywords, forming 7 distinct clusters with a modularity score above 0.3. The silhouette value, exceeding 0.7, indicates high consistency within each cluster. The identified clusters were labeled: #0 for cognitive control, #1 for theta, #2 for epilepsy, #3 for graph theory, #4 for machine learning, and #5 for neurofeedback. Keyword bursts indicate trends in field development. Figure 5C showcases the 25 keywords with the most dramatic citation increases, topped by “2 subtypes” with a burst score of 13.01, “machine learning” at 7.26, and “age” at 6.87. Furthermore, the terms “neurofeedback,” “deep learning,” “dynamics,” “machine learning,” “connectivity,” “classification,” and “efficacy,” demonstrated sustained levels of interest up to the year 2023, indicating current research trends.

Table 4 Top 20 keywords about this research field from 2004 to 2023.

Rank	Keywords	Count	Rank	Keywords	Count	
1	ADHD (attention deficit hyperactivity disorder)	689	11	methylphenidate	41	
2	EEG (electroencephalography)	546	12	adults	33	
3	ERP (event-related potentials)	104	13	working memory	28	
4	children	89	14	biomarkers	27	
5	neurofeedback	89	15	cognition	25	
6	theta/beta ratio	58	16	executive functions	25	
7	attention	55	17	machine learning	25	
8	epilepsy	52	18	depression, ASD (Alzheimer’s disease)	24	
9	quantitative EEG	52	19	adolescents	23	
10	autism spectrum disorder	49	20	response inhibition	22	

Figure 5. Keyword analysis for research of EEG applications in ADHD (2004–2023). (A) Distribution of co-occurring keywords; (B) Time-zone view of keyword co-occurrence; (C) Top 25 keywords with the strongest citation bursts. ADHD = attention deficit hyperactivity disorder, EEG = electroencephalography.

3.6. Analysis of references

Table 5 lists the top 10 co-cited references, with the American Psychiatric Association (APA) cited 144 times, Mittal VA et al in Psychiatry Research at 55, and Arns M et al in Journal of Attention Disorders with 44 citations. As depicted in Figure 6A, the reference network comprises 940 nodes and 4076 links, reflecting co-citation ties among references. Node connectivity correlates positively with citation frequency. Cluster analysis reveals high modularity (Q = 0.7785) and silhouette scores (S = 0.9147), underscoring robust clustering within the network. Figure 6B delineates the principal 17 clusters of the co-cited reference, signifying the dominant research subjects, topped by clusters for “P300” (#0), “ADHD” (#1), “neurofeedback” (#2), “theta” (#3), and “machine learning” (#4). Citation bursts in references can signal the emergence of academic hotspots and predict trends. Figure 6C identifies the 25 references with the most significant citation bursts, topped by Barry RJ et al’s 2003 article in Clinical Neurophysiology with a 20.35 score. APA’s DSM-5™ is a close second, with a burst of 17.88. Figure 7 presents a timeline illustrating the evolution of the most significant clustering terms identified in the study, among which the most recent clustering terms are: “P300” (#0), “theta” (#3), and “machine learning” (#4).

Table 5 Top-10 most co-cited references from 2004 to 2023.

Rank	Title	Author	Year	Journal	Co-citation frequency	
1	Diagnostic and statistical manual of mental disorders: DSM-5™, 5th ed.	APA	2013	American Psychiatric Association	144	
2	Dyskinesias, tics, and psychosis: Issues for the next Diagnostic and Statistical Manuel of Mental Disorders	Mittal VA	2011	Psychiatry Research	55	
3	A decade of EEG Theta/Beta Ratio Research in ADHD: a meta-analysis	Arns M	2013	Journal of Attention Disorders	44	
4	Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis	Van Doren J	2019	European Child & Adolescent Psychiatry	35	
5	A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography	Barry RJ	2003	Clinical Neurophysiology	35	
6	Quantitative EEG in Children and Adults With Attention Deficit Hyperactivity Disorder: Comparison of Absolute and Relative Power Spectra and Theta/Beta Ratio	Markovska-Simoska S	2017	Clinical EEG and Neuroscience	34	
7	Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity, and hyperactivity: a meta-analysis	Arns M	2009	Clinical EEG and Neuroscience	30	
8	Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan	Liechti MD	2013	Brain Topography	30	
9	Evaluation of neurofeedback in ADHD: the long and winding road	Arns M	2014	Biological Psychology	29	
10	The quantitative EEG theta/beta ratio in attention-deficit/hyperactivity disorder and normal controls: sensitivity, specificity, and behavioral correlates	Ogrim G	2012	Psychiatry Research	28	
ADHD = attention deficit hyperactivity disorder, EEG = electroencephalography.

Figure 6. Analysis of co-cited references in the field of EEG applications in ADHD (2004–2023). (A) The network of co-cited references; (B) Clustering visualization map of the co-cited references; (C) Top 25 references with the strongest citation bursts. ADHD = attention deficit hyperactivity disorder, EEG = electroencephalography.

Figure 7. Timeline visualization map of the co-cited references in the field of EEG applications in ADHD (2004–2023). ADHD = attention deficit hyperactivity disorder, EEG = electroencephalography.

4. Discussion

As a neurodevelopmental disorder, ADHD significantly affects individuals, impacting social skills, career, professional abilities, cognitive functions.[15] Specifically, ADHD may lead to poor school performance, joblessness, marital issues, and criminal activity. Diagnosis currently depends solely on clinical assessment without objective tests.[16] Fortunately, the application of EEG offers a glimmer of hope for this dilemma. An increasing number of researchers are engaging in the study of EEG biomarkers related to ADHD, hoping to discover an objective and reliable diagnostic indicator, thereby providing a more scientific basis for the diagnosis and treatment of ADHD.[17] This study conducted a bibliometric analysis of the application of EEG in ADHD using the WoSCC database, aiming to provide researchers in this field with a comprehensive overview of global research trends and key hotspots.

4.1. General information

Between 2004 and 2023, a total of 1162 articles related to the application of EEG in ADHD were published in 357 academic journals by 4873 authors affiliated with 1751 institutions across 69 different countries/regions, as recorded in the WoSCC database. The significant increase in publications reflects the growing interest in this field, which may attract more attention in the future.

The visual analysis shows the US leading in this field, with 24.27% of total papers and the highest centrality of 0.29, highlighting its key role in global collaboration. Notably, China and Iran emerge as the sole developing countries in the top 10 contributors’ list, boasting 135 and 54 publications, respectively. In terms of centrality value, developed nations show greater involvement than developing ones. Economic and policy differences likely account for the regional knowledge disparities in the application of EEG in ADHD research.

Globally, 1751 institutions have conducted research in this field, with 4 of the top 10 institutions being from Germany, yet the University of London leads in terms of contributions to publications. Among these institutions, the University of London, the University of California System, and Harvard University are at the forefront in terms of centrality, likely because the field involves multiple disciplines, and these universities are comprehensive universities with extensive coverage of subjects and strong research capabilities. Figure 3 illustrates the diverse participation of various countries and institutions in collaborations, highlighting the importance of prioritizing cooperation among nations and institutions with shared research interests to advance the field.

The author’s analysis revealed that Barry RJ and Clarke AR, as well as McCarthy R and Selikowitz M, have the same number of papers and citations. After repeated verification of the research data and further in-depth investigation, it was found that this was not a coincidence. They are a team from Australia that has made outstanding contributions to the field of EEG in ADHD. Barry RJ and Clarke AR are professors at the School of Psychology at the University of Wollongong. They research the electrophysiological characteristics of EEG in ADHD and have coauthored multiple papers investigating EEG activity in ADHD patients and the effects of pharmacological treatments. McCarthy R and Selikowitz M are pediatricians and developmental pediatricians at the Sydney Developmental Clinic. Adam R. Clarke and Robert J. Barry primarily collaborate in scholarly research, while Rory McCarthy and Mark Selikowitz apply these research findings in clinical practice. Through their close collaboration, they collectively advance the understanding and treatment of ADHD.[18,19] From Figure 4D, it can be observed that Professor Sun L has published a significant number of articles in the field of EEG for children with ADHD in recent years. She has recently been committed to the research of remote neurocognitive intervention and neurofeedback training.[20,21]

Journal publication trends and research fields enable researchers to stay informed about the latest developments in their field, providing direction for researchers in terms of reading and submitting their work. Most of the top 10 journals are dedicated to neurophysiology and psychophysiology. However, among the top 10 journals, only 3 have reached the Q1 ranking, and only 2 have an Impact Factor exceeding 3.0. This indicates that the field still requires a substantial amount of research to catalyze a qualitative change.

4.2. Emerging topics

Reference citation bursts in evolving references reveal field dynamics. The paper by Barry RJ, published in 2003 and focusing on the EEG analysis of ADHD, stands out as the most citation burst work in the field.[22] It emphasizes the importance of EEG in diagnosing and researching the pathological processes of ADHD and provides direction for future research. The DSM-5™ published by APA is the next most cited in citation bursts.[4] Since its publication, the DSM-5™ has become the professional standard in the field of psychiatry, guiding the development of emerging measurements and models. Figure 6C illustrates that the citation landscape has evolved over time, with 4 new articles currently experiencing a surge in citations, a trend that continues through 2023.[23–26] The comprehensive assessment of keywords and citation analysis indicates that the core trends in EEG research for ADHD include “TBR,” “P300,” “neurofeedback,” and “deep learning,” which will drive the development of the field in the future.

The theta/beta ratio (TBR) has garnered significant attention due to its potential as a biomarker for ADHD.[27] Zhang et al[28] found that the TBR is positively correlated with symptoms of inattention in patients with ADHD. The link between TBR and attention is rooted in neurophysiological processes. Theta and alpha bands prevail at rest, with a switch to beta during cognitive tasks. Higher TBR in ADHD may relate to attention difficulties.[2] Additionally, Duric et al[29] also found that children with ADHD exhibit significant differences in TBR and frontal theta activity compared to typically developing children. TBR shows potential in assisting the diagnosis of ADHD, but its effectiveness and precision in the diagnostic process are still open to debate. Some studies indicate that TBR is a key indicator for ADHD in children, but the findings in adults are inconsistent.[30] Additionally, renowned expert Clarke et al[31] in the field has noted in a review article that the sensitivity and specificity of TBR for diagnosing ADHD vary across studies. Despite the controversy, TBR still has its unique significance in neurophysiological measurements. Further research is needed to explore its potential applications in the diagnostic and assessment process.

The P300 waveform in event-related potentials is an electrophysiological marker that has garnered significant attention in the evaluation and treatment of ADHD, and studies have suggested that it may serve as a potential therapeutic target for ADHD.[32] Appearing as a positive voltage spike roughly 300 milliseconds post-target stimuli in the central parietal area, the P300 waveform is intricately linked to the focus of attention and the handling of sensory inputs.[33] Kaiser et al[26] found that in the Go/No Go task, compared to the healthy control group, children and adults with ADHD exhibited prolonged P300 latencies in both Go and No Go trials, and a significant reduction in P300 amplitude during the No Go trials. The research by Morand-Beaulieu et al[34] also arrived at similar conclusions, including a reduction in theta power in the midline frontal region, as well as delayed latencies of the N200 and P300 components. These findings further underscore the potential neurophysiological differences in cognitive inhibition and attention allocation processes in children with ADHD. Additionally, a recent study found that neurostimulant medications can normalize the amplitude and latency of the P300 waveform in patients with ADHD.[26] This phenomenon reveals the potential of the P300 waveform as an indicator for assessing the therapeutic response in ADHD and other neurodevelopmental disorders.

Neurofeedback training measures the brain activity of subjects and processes the measurement signals through technical means to extract key parameters, which are then fed back to the subjects in real-time in the form of visual or auditory information. The core goal of this technique is to teach participants how to adjust these key parameters, thereby enabling the regulation of the subject’s brain and cognitive activities.[35] This technology opens up new possibilities for non-pharmacological treatment of ADHD. Studies by Arns et al[36] indicate that neurofeedback therapy has shown significant efficacy in the treatment of ADHD, with remission rates of 32% to 47% and sustained effects over a period of 6 to 12 months. A randomized clinical trial conducted by Shojaei et al[37] also found that neurofeedback has significant effectiveness in treating children’s attention deficit disorders and hyperactivity symptoms. Some scholars have pointed out that research on neurofeedback therapy for ADHD often relies on retrospective reports, case series, and data from small samples, which have their limitations. Moreover, uncertainties in diagnosis, ambiguous definitions, the presence of multiple comorbidities, lack of standardized treatment protocols, and nonstandard blinding methods may all lead to biases in research.[38] In summary, the work on neurofeedback therapy for ADHD is challenging and has a long way to go.

In recent years, deep learning technology has made significant progress in diagnosing ADHD through EEG.[39] Due to the high dimensionality and noise typically associated with EEG data, deep learning methods are highly effective in processing and extracting these data, eliminating the complex steps of manual extraction and improving the predictive accuracy of models.[40] Recently, Jahani et al[39] have applied deep learning models to diagnose ADHD based on EEG data, with the best average accuracy reaching 96.8%. Furthermore, Dr Khare SK’s team has developed a deep learning model that achieves an accuracy and sensitivity rate of 99.81% and 99.78%, respectively.[41] These studies in the field of deep learning not only assist in the early and rapid diagnosis of ADHD but also significantly reduce treatment costs and the number of patients subjected to complex diagnostic procedures. Despite this, deep learning technology still faces many challenges. The training of deep learning models heavily relies on a large amount of high-quality data. Unfortunately, the number of publicly available EEG datasets related to ADHD is limited, which greatly restricts the models’ generalization capabilities. Moreover, ADHD patients exhibit significant individual differences in symptoms and EEG characteristics, necessitating the urgent development of personalized and robust models. The author believes that integrating deep learning into real-time EEG analysis for instant feedback could be a key trend in future research.

5. Limitations

The study has limitations due to bibliometric analysis. It relied solely on the WoSCC database for literature, risking incomplete and biased study selection. Also, the focus on English articles and reviews may have limited the findings’ comprehensiveness. In conclusion, biases may persist due to diverse keyword expressions, identical author names, and the dynamic nature of the WoSCC database, despite our adherence to protocols. We must strive to mitigate these in future studies. Even so, our research likely captures the current state and trends in the field.

6. Conclusion

Over the past 2 decades, the academic community has shown a growing interest in the application of EEG in ADHD. The USA, University of London, and Barry RJ led in productivity and impact, while the Clinical Neurophysiology topped in publication volume and citations. As research progresses, key clustering terms such as “cognitive control,” “theta waves,” “epilepsy,” “graph theory,” “machine learning,” and “neurofeedback” have emerged in this field, forming the foundational framework of the current core research areas. In addition, some emerging research frontiers have gradually come to light, including “TBR,” “P300 wave,” “neurofeedback,” and “deep learning.” Overall, this is the inaugural bibliometric analysis of the application of EEG in ADHD research, providing a systematic framework to guide future studies in the field, thereby facilitating and promoting further development in this area.

Acknowledgments

The authors thank and express their gratitude to the experts in the fields of neurophysiology and statistics, for them providing guidance and advice on the application of EEG in ADHD, CiteSpace, and VOSviewer.

Author contributions

Conceptualization: Yijun Deng.

Data curation: Jie Wei, Siyuan Sun, Wei Chen.

Methodology: Ben Liu.

Visualization: Xian Liu.

Writing – original draft: Ben Liu, Xian Liu.

Writing – review & editing: Ben Liu, Yijun Deng.

Supplementary Material

Abbreviations:

ADHD attention deficit hyperactivity disorder

APA American Psychiatric Association

EEG electroencephalography

TBR theta/beta ratio

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Liu B, Liu X, Wei J, Sun S, Chen W, Deng Y. Global research progress of electroencephalography applications in attention deficit hyperactivity disorder: Bibliometrics and visualized analysis. Medicine 2024;103:38(e39668).
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