A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries

Abstract

Introduction

EEGLAB is a widely used software for analyzing electroencephalography (EEG) datasets, with over 20 years of global use. This bibliometric study investigates EEGLAB publications in the Asia-Pacific and Arabian regions, focusing on Scopus and Web of Science (WoS) indexed sources, to understand regional contributions and trends in EEG research across 80 countries and territories.

Methods

Bibliometric analysis was conducted using the Bibliometrix package in R, focusing on citations from WoS and Scopus indexed sources. The study covers data from the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) and West Asia (ESCWA), including 58 ESCAP countries and 22 ESCWA countries, with data collected until March 2024. Bibliometric indices such as Lotka's law, Bradford's law, co-citation networks, and in-depth historiography were analyzed to explore global and regional trends.

Results

EEGLAB's main article has received 22 298 citations on Google Scholar, with 14 958 (67.1%) citations in WoS and 15 827 (70.1%) in Scopus. The top ten WoS countries by citations are China, Australia, Japan, Republic of Korea, India, Iran, Russian Federation, Singapore, New Zealand, and Malaysia/Türkiye. Recent research themes include emotion recognition, driver distraction detection, deep learning, schizophrenia detection, and the cognitive impacts of COVID-19. China and the USA are the leading international collaborators.

Conclusions

Approximately 35% of WoS documents and 41% of authors are from the ESCAP and ESCWA regions. The average citation per document in these regions is half of the global average. The study highlights EEGLAB's growing role in EEG research and collaborative trends.

Keywords

EEGLAB bibliometric analysis Asia-Pacific Arabian states Scopus Web of Science functional data analysis

Key Messages

Bibliometric analysis of all EEGLAB Citations from Scopus & Web of Science

Focusing on 80 countries & territories including the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) and West Asia (ESCWA)

Estimation and Comparison of Lotka’s Law Coefficients estimation for Authors with Functional Data Analysis in Bibliometric Studies

Provided the Bradford Law, The Most Global Cited Documents, The Co-Occurrence Network of WoS subject, The Co-Citation Network Papers, Collaboration Network Between Institute, The Country Collaboration Map and Authors production over time

The study includes two extensive appendices: 1) Further analysis for each subregion and 2) in-depth historiographies with summaries

Specific analysis for China, Japan, India, Russian Federation and Iran in appendix

Introduction

Since Hans Berger's development of the electroencephalogram (EEG) in 1929, it has become a cornerstone diagnostic tool in neuroscience, widely utilized around the world from as early as 1936.^1–3 Beginning in the 1990s, a range of academic software applications were developed for EEG-based brain mapping,⁴ including EEGLAB,⁵ FieldTrip,⁶ and SPM,⁷ along with more recent advances in Python, such as MNE.⁸ EEGLAB, an open-source platform, was developed by a team led by Professors Arnaud Delorme and Scott Makeig from the Swartz Center for Computational Neuroscience, Institute for Neural Computation at the University of California, San Diego.⁵ Key features of EEGLAB include Independent Component Analysis (ICA), time-frequency analysis, various artifact removal methods, a user-friendly graphical user interface (GUI), multi-format data importation, and interactive plotting. To date, EEGLAB has been downloaded over 350 000 times by more than 15 265 researchers, with over 160 available plug-ins in 2024.⁹

More than 12 700 research articles and documents indexed in Web of Science have cited EEGLAB papers worldwide, with an estimated international co-authorship rate of 37.27%.¹⁰ As highlighted in various bibliometric analyses—such as those in Construction Health and Safety Management,¹¹ Visualizations in Computer Graphics,¹² Brain-Computer Interface,¹³ and Good Scientific Practice¹⁴—EEGLAB stands out as the primary software used in these fields.

Bibliometric studies focusing on geographically specific regions have become increasingly important in neuroscience. For instance, previous research has analyzed neuroscience publications in Saudi Arabia,¹⁵ Turkey,¹⁶ Iran,¹⁷ Latin America,¹⁸ Brazil,¹⁹ neurosurgical research in Southeast Asia,²⁰ Egyptian neurosurgical publications,²¹ neurodegenerative disorders in Arab countries,²² and Alzheimer's disease globally and in China.²³ However, these studies have predominantly focused on research topics rather than the tools and software utilized, although the latter is a common focus in statistical software analyses.^24,25 Typically, the most cited articles come from the United States, North America, and European countries, with other regions receiving less attention.¹⁰ To date, however, no bibliometric analysis has specifically examined EEGLAB paper citations in regions such as the Asia-Pacific or the Arabian states, which together encompass 80 countries and territories. The importance of these regions is exemplified by initiatives such as the International Brain Research Organization (IBRO) Asia-Pacific Regional Committee (APRC),²⁶ the Synchrotron for Neuroscience – an Asia Pacific Strategic Enterprise (SYNAPSE),²⁷ Arabs in Neuroscience²⁸ and Federation of Asian Oceanian Neuroscience Societies (FAONS), which includes over 10 national neuroscience societies.²⁹ In this study, we analyzed these regions using the SCOPUS and Web of Science (WOS) databases, along with the bibliometrix R package.³⁰

Material and Methods

Selected Regions

The countries and territories in two of the five regional commissions under the jurisdiction of the United Nations Economic and Social Council are considered.³¹ They are including: 1-United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) with 58 countries and territories.³² They divided into 5 Asia-Pacific subregions: I) East and North East Asia (ENEA): China, Democratic People's Republic of Korea, Hong Kong (China), Japan, Macao (China), Mongolia and Republic of Korea. II) North and Central Asia (NCA): Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Russian Federation, Tajikistan, Turkmenistan and Uzbekistan. III) Pacific (PACIFIC): American Samoa, Australia, Cook Islands, Fiji, French Polynesia, Guam, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Northern Mariana Islands, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu and Vanuatu. IV) South East Asia (SEA): Brunei Darussalam, Cambodia, Indonesia, Lao People's Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste and Vietnam. V) South and South West Asia (SSWA): Afghanistan, Bangladesh, Bhutan, India, Iran (Islamic Republic of), Maldives, Nepal, Pakistan, Sri Lanka and Türkiye. And 2-The United Nations Economic and Social Commission for West Asia (ESCWA) or Arab States with the following 22 countries and territories³³: Algeria, Bahrain, Comoros, Djibouti, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, State of Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syrian Arab Republic, Tunisia, United Arab Emirates and Yemen.

Databases

The Web of Science (WoS) and Scopus are used as two citation databases. The search strategy considers all citations grouped by regions, subregions, and countries of the Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods. 2004 Mar 15;134(1):9–21.⁵ The date of searching and downloading was 03/28/2024.

Statistical and Data Analysis

Bibliometric analysis is done with Bibliometrix,³⁰ functional data analysis with fda.usc for comparison between Lotka's Law curves,³⁴ and artificial neural networks using nnet and NeuralNetTools for stuyding the average citation per doc³⁵ packages in R software with POSIT (https://www.r-project.org/).³⁶ ChatGPT (GPT-4) was utilized as an invaluable tool for editing, data collection, and summarizing complex information (https://openai.com/chatgpt).

Results

The Descriptive Statistics

As of 03/28/2024, EEGLAB software has accumulated 22 298 citations in Google Scholar.⁵ In comparison, there are 14 958 citations (≈ 67.1% of Google Scholar's total) in Web of Science (WoS) and 15 827 citations (≈ 70.1% of Google Scholar's total) in Scopus. According to Table 1, the total number of citations in WoS across all selected regions is 4836 (≈ 21.6% of the total), with the following distribution: ENEA (3168 citations, ≈ 14.2%), NCA (172 citations, ≈ 0.77%), PACIFIC (836 citations, ≈ 3.75%), SEA (276 citations, ≈ 1.23%), SSWA (564 citations, ≈ 2.52%), and Arab states (155 citations, ≈ 0.69%). These citations were published between 2004 and 2024 across 975 sources (journals, conference proceedings, etc), authored by 12 140 researchers, and referencing a total of 134 022 unique sources. ENEA, comprising China, Japan, and the Republic of Korea, is a strong research cluster with a notable average citation per document of 12.54 and a solid document output of 3168. However, compared to other subregions like NCA, PACIFIC, and ESCWA, ENEA has a relatively lower international co-authorship rate of 35.95%, and its annual growth rate of 15.37% lags behind more dynamic regions like SEA (15.15%) and PACIFIC (14.11%). ESCWA, on the other hand, stands out with a high international co-authorship rate of 81.29% and a good average citation per document of 13.12. However, its document output of just 155 and relatively low author count of 564 suggest limited research activity and fewer contributions from authors in the region. The table shows that the USA leads globally, while China tops the ESCAP & ESCWA region. In ENEA, China and Japan are the leaders, with Russia at the top in NCA. Australia and New Zealand rank first and second in the PACIFIC, while Singapore and Malaysia lead in SEA. India and Iran are the top countries in SSWA, and Saudi Arabia and the UAE dominate ESCWA. Additionally, the Republic of Korea and Türkiye are among the top contributors in their regions.

Table 1.

Bibliometric Indices by Database and Subregion (as of March 28, 2024).

DatabaseSubregions	WoS								Scopus
	All		Subregions						All
	World*	ESCAP &ESCWA	ENEA	NCA	PACIFIC	SEA	SSWA	ESCWA	World
1% Top country in region **	USA	China	China	Russia	Australia	Singapore	India	Saudi Arabia	USA
Indices
From (Year)	2004	2004	2005	2005	2004	2008	2007	2007	2004
To (Year)	2023	2024	2024	2023	2024	2024	2024	2024	2024
Documents	12 700	4836	3168	172	836	276	564	155	15 827
Sources	1125	975	652	88	291	159	289	102	2072
Authors	29 125	12 140	7665	538	2569	917	1673	564	33 070
Keywords	19 062	9375	6729	557	2113	848	1719	541	21 384
References	279 617	134 022	94 266	8576	33 891	11 719	22 987	7057	763 160
Annual Growth Rate	28.12%	20.85%	15.37%	15.31%	14.11%	10.58%	15.15%	9.93%	18.63%
International Co-Authorship	37.27%	42.39%	35.95%	56.40%	65.79%	71.01%	48.76%	81.29%	34.8%
Co-Authors per Doc	4.89	5.42	5.58	5.28	5.69	5.41	4.70	5.28	5.14
Document Average Age	5.03	4.88	4.66	6.97	5.36	5.64	4.34	4.86	5.85
Average Citation per Doc	22.51	13.97	12.54	19.74	19.21	19.85	12.44	13.12	23.02

*They were extracted from¹⁰ until 08/27/2023; Abbreviations: United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), East and North East Asia (ENEA),North and Central Asia (NCA),Pacific (PACIFIC),South East Asia (SEA),South and South West Asia (SSWA),United Nations Economic and Social Commission for West Asia (ESCWA), ** According to the number of WOS and SCOPUS Articles.

Various factors influence citation rates, a separate analysis is needed to investigate the main reasons.^37,38 Nevertheless, we found that Document Average Age, Co-Authors per Document, and International Co-Authorship have the highest relative importance across the three algorithms—Garson's algorithm, Olden's algorithm, and Lek's profile method—in a single-layer fitted artificial neural network model for these six regions.³⁵ In Figure 1, the Olden's algorithm plot shows the positive and negative relationships of variables with the outcome. Additional plots are available in Appendix A, Part K.

Figure 1.

The Variable Importance plot. The x-Axis is th Variable Names and y-Axis is the Variable Importance Weights with Olden's Algorithm.

The list of citations, grouped by country name and region, is presented in Appendix A, Table A-1 for both the WoS and Scopus databases. According to this table, the top ten countries based on WoS citations are China (2257), Australia (755), Japan (574), the Republic of Korea (392), India (231), Iran (191), the Russian Federation (163), Singapore (151), New Zealand (103), and both Malaysia (79) and Türkiye (79). The difference in the number of citations between WoS and Scopus for each country is calculated as Diff = #WoS − #Scopus. Countries with a citation difference (Diff) of 40 or more include Hong Kong (China), India, Japan, China, the Russian Federation, Iran, and Macao (China). A more detailed analysis, including a country-specific profile based on Scopus citations for these specific countries, is provided in Appendix A, Part A and Part B.

Lotka's Law Coefficient

Lotka's Law coefficient, which describes the frequency of publication by authors in a field as an inverse square law, where the number of authors publishing a certain number of articles is proportionate to the number of authors publishing a single article, is estimated using the lotka() function in the R bibliometrix package.^30,39 Table C1 in appendix A presents the Lotka's statistics across different regions. The Beta values range from 2.12 in ENEA to 2.71 in SSWA, indicating some variability in author productivity patterns across regions. The R² values are consistently high, ranging from 0.93 in Arab States to 0.99 in ENEA, suggesting a strong fit of the data to Lotka's Law. The p-values for all regions, such as 0.98 for ENEA and 0.75 for NCA, are greater than 0.05, indicating that there is no significant difference between the observed and expected distributions of author productivity. Overall, the All regions category, with a Beta of 2.22, R² of 0.99, and p-value of 0.75, reflects a solid adherence to Lotka's Law across the entire dataset. To compare Lotka curves between regions, two groups were formed: 1) ENEA, PACIFIC, and SEA representing East Asia regions, and 2) NCA, SSWA, and ESCWA representing Central and West Asia regions. The Lotka curves were compared using an ANOVA test for functional data with the fanova.onefactor() function from the fda.usc package.^34,40 The p-values based on 1000 bootstrap resamples for the number and proportion of authors were 0.174 and 0.226, respectively, indicating no significant statistical difference between these two groups (Figure 2) and (Appendix A, Part C).

Figure 2.

The Functional ANOVA Result Plots. Left: The Functional Mean for Group 1 in Dark Grey with Triangle Symbols (ENEA, PACIFIC, and SEA or “East Asia”) and Group 2 in Light Grey with Circle Symbols (NCA, SSWA, and ESCWA or “Central or West Asia”), with a Solid Black Line Representing the Overall Functional Mean and Dashed Lines for Each Region in the Background. Center: The 1000 Bootstrap Resamples. Right: The Density of Bootstrap Resamples and the p-Value.

Bradford Law

The core sources based on Bradford's Law are estimated using the bradford() function for all six regions, including prominent journals such as Neuroimage, Frontiers in Human Neuroscience, Scientific Reports, Frontiers in Neuroscience, Psychophysiology, PLoS One, International Journal of Psychophysiology, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Neuropsychologia, Journal of Neural Engineering, Brain Sciences, Biological Psychology, Frontiers in Psychology, Clinical Neurophysiology, and Cerebral Cortex. The core sources for each region are detailed separately in Appendix A, Part D.

The Most Global Cited Documents

Table 2 presents the two most globally cited documents for each region, grouped by all-time citations and those published after 2020, as we aim to find the most recent papers reflecting changes in technologies trends, based on the SCOPUS database. The table includes the total citation (TC) frequency and average citations per year for each document. Note that some authors have multiple affiliations across different countries or their affiliation has changed over time. Therefore, only the affiliation listed in the published papers is considered in the table.

Table 2.

The Most Global Cited Documents Group by Region and Period Based on SCOPUS.

Region	Period	Ref	TC		Article Title/Description
Region	Period	Ref	#	per Year	Article Title/Description
ENEA	All Time	⁴¹	558	34.88	Online Multi-Channel Steady-State Visual Evoked Potential (SSVEP)-based BCI with Canonical Correlation Analysis (CCA)
	All Time	⁴²	270	27.00	Online Multi-View Transfer Takagi-Sugeno-Kang (TSK) fuzzy system for estimating EEG-Based Driver Drowsiness
	≥2020	⁴³	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
	≥2020	⁴⁴	85	17.00	Bispectrum based channel selection (BCS) method for motor imagery (MI)-based BCI
NCA	All Time	⁴⁵	169	10.56	Synchronization Of Event-Related Delta and Theta In Explicit And Implicit Emotion Processing
	All Time	⁴⁶	162	11.57	Relations between Heart–brain interactions and somatosensory perception and evoked potentials
	≥2020	⁴⁷	104	20.80	Independent Component Analysis (ICA) in default mode network and EEG alpha oscillations
	≥2020	⁴⁴	85	17.00	Bispectrum based channel selection (BCS) method for motor imagery (MI)-based BCI
PACIFIC	All Time	⁴⁸	343	19.06	Statistical Significance that EEG frequencies above 20 Hz are contaminated by electromyogram (EMG) in the presence and absence of complete neuromuscular blockade, sparing the dominant arm trial.
	All Time	⁴⁹	229	19.08	Validation of the Emotiv EPOC^® EEGGaming System for Measuring ResearchQuality Auditory for Auditory Event-Related Potentials (ERPs)
	≥2020	⁴³	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
	≥2020	⁵⁰	80	20.00	The ICA method improves the quality of EEG mobile and stationary experiments
SEA	All Time	⁴²	270	27.00	Online Multi-View Transfer Takagi-Sugeno-Kang (TSK) fuzzy system for estimating EEG-Based Driver Drowsiness
	All Time	⁵¹	219	24.33	A Review about Detection and Removal Artifacts in Scalp EEG
	≥2020	⁵²	77	19.25	Deep Convolutional Generative Adversarial Networks (DCGANS) for Generating Artificial EEG in BCI
	≥2020	⁵³	70	14.00	Decoding Movement-Related Cortical Potentials (MRCP) based Brain-Machine Interface (BMI)
SSWA	All Time	⁵⁴	397	28.36	Scalable Tensor Factorizations, CANDECOMP/PARAFAC (CP), with Missing Data
	All Time	⁵¹	219	24.33	A Review about Detection and Removal Artifacts in Scalp EEG
	≥2020	⁵⁵	113	28.25	A review of Machine Learning Methods for EEG signal Processing
	≥2020	⁵⁶	106	21.20	A Review about Biomarkers (also EEG based) for Early diagnosis of Alzheimer's disease based on the International Federation of Clinical Neurophysiology (IFCN)
ESCWA	All Time	⁵⁷	177	17.70	EEG Delta–Beta Coupled Oscillations Underlie Temporal Prediction Accuracy
	All Time	⁵⁸	143	10.21	A Trial about Listening Effort with capturing EEG
	≥2020	⁵⁹	27	5.40	Using Higuchi's fractal dimension (HFD) and sample entropy (SampEn) methods to study the Biomarkers of major depressive disorder (MDD) with EEG.
	≥2020	⁶⁰	25	5.00	EEG mobility artifact removal for ambulatory epileptic seizure prediction applications
All Six Regions
	All Time	⁴¹	558	34.88	Online Multi-Channel Steady-State Visual Evoked Potential (SSVEP)-based BCI with Canonical Correlation Analysis (CCA)
	All Time	⁵⁴	397	28.36	Scalable Tensor Factorizations, CANDECOMP/PARAFAC (CP), with Missing Data
	≥2020	⁴³	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
	≥2020	⁵⁵	113	28.25	A review of Machine Learning Methods for EEG signal Processing

Abbreviations: TC (Total Citations).

The Co-Occurrence Network of WoS Subject

The Co-Occurrence Network of WoS (Web of Science) subjects identifies relationships between academic fields based on their co-occurrence in research papers. This network includes various clusters, such as subjects in Neurosciences, Psychology, Computer Science, Engineering, Statistics, Probability, Mathematics, Environmental Studies, Transportation and Ergonomics, Applied Physics, Material Sciences, Medicine and Health Care, Energy and Fuels, Language and Linguistics, Educational Research, Sports Science, Management, Library Science, Biology, and Robotics. These clusters cover diverse disciplines, with further analysis provided in the appendix A, part E.

The Co-Citation Network Papers

A co-citation network is a method used to visualize and analyze the relationships between scholarly papers based on how often they are cited together in other research. This network helps identify key papers that influence a field and understand how research evolves over time. In the context of EEG research,⁵ plays a pivotal role as a central reference, with significant connections to foundational works such as,^61,62 and.⁶³ The network also includes later influential papers like,^64,65 and,⁶⁶ which collectively track the development of EEG signal processing, analysis techniques, and cognitive neuroscience. The latest addition to this co-citation network is,⁶⁷ which introduces the ICLabel MATLAB plug-in, offering a significant advance in automatic EEG artifact classification. These interconnected references illustrate the progression of EEG methodologies and their application to understanding brain activity, making the co-citation network a valuable tool for mapping the intellectual landscape of the field such as ICLabel Python package.⁶⁸ Other papers in this network are^6,69–83: and ⁸⁴. (Appendix A, Part F provides a separate analysis for each region)

Collaboration Network Between Institute

The Collaboration Network Between Institutes refers to the connections formed through joint research, co-authored papers, and collaborative projects among academic institutions. In this region, three main collaboration clusters are identified: 1) the Chinese Academy of Sciences, University of Chinese Academy of Sciences (CAS), Southwest University, Beijing Normal University, Shanghai Jiao Tong University, Shenzhen University, and Peking University; 2) Zhejiang University and National University of Singapore; and 3) the University of California, UC San Diego, Monash University, Seoul National University (SNU), University of Toronto, and University of Tokyo. Additional universities are present in other subregions, including the Russian Academy of Sciences and HSE University in NCA, University of Technology Sydney and National Yang Ming Chiao Tung University in the Pacific, the Indian Institute of Technology (IIT), Shahed University, and Islamic Azad University in SSWA, and the Institut National de la Santé et de la Recherche Médicale (INSERM), Lebanese University, and Université de Rennes in ESCWA. Further details on other universities are available in Appendix A, Part G.

The Country Collaboration Map

The Figure 3 shows the frequency of collaborations between various countries, highlighting key international partnerships. China and the USA are the most frequent collaborators, with 308 joint activities, indicating a strong partnership. Australia and the USA follow with 132 collaborations, while China also has notable collaborations with the United Kingdom (131), Australia (102), Germany (89), and Canada (84). Australia collaborates not only with the USA but also with Germany (79) and the United Kingdom (77). Other significant collaborations include the USA and South Korea (77) and Japan and the USA (70). Overall, China, the USA, and Australia emerge as major hubs for international collaborations, with strong research and joint efforts occurring between these countries. The subregion analysis are in the appendix A, Part H.

Figure 3.

The Country Collaboratin Map of Studied Regions.

Authors Production Over Time

Chin-Teng Lin from the University of Technology Sydney and Li Hu (胡理) from the Institute of Psychology, Chinese Academy of Sciences are ranked first and second, respectively, based on the number of articles published and total citations per year from 2006 to 2024. Their consistent research output and influence in their fields are reflected in these rankings. The production over time for other authors in each region is provided in Appendix A, Part I.

The Further Analysis

The Further analysis, grouped by subregions, including Keyword Plus Trend Topics and historiography with a summary of the main findings, can be found in Appendix A, Part J and Appendix B, respectively.

Discussion

In the year marking the 20th anniversary of EEGLAB (2004-2023), the EEGLAB Newsletter issue 17, published in February 2024,⁸⁵ and a bibliometric analysis of all 12 700 Web of Science (WoS) citations of EEGLAB software up until 8/27/2023 were conducted.¹⁰ The updated analysis, incorporating both Scopus and WoS citations, now totals 15 827 citations as of 03/28/2024. This new data shows that EEGLAB was published in 2072 sources, written by 33 070 authors, cited 763 160 references, and presented 21 384 keywords. The annual growth rate of publications is 18.63%, and the international co-authorship rate stands at 34.8%. Additionally, a comparison of total citations from the World, ESCAP, and ESCWA regions revealed¹⁰ that approximately 35% of all WoS documents and 41% of authors came from these regions. However, the average citation per document globally is twice that of these regions.

The significance of countries in the Asia-Pacific region for neuroscience was previously emphasized. In this regard, Table 3 compares the top 10 articles with the highest number of total global citations after 2022 between the ESCAP & ESCWA regions in the WoS database, as well as all countries worldwide in the SCOPUS database. Focusing on articles published after 2022 allows us to identify recent trends and emerging research, driven by technological advancements. The first three articles from WoS in the ESCAP & ESCWA regions and the first three from SCOPUS globally present common themes centered on EEG-based applications in emotion recognition,⁸⁶ driver distraction detection,⁸⁷ and mental health diagnosis. Both sets of articles utilize advanced deep learning models and EEG data to enhance recognition accuracy, with a strong emphasis on using spatial and temporal information for more accurate predictions. However, differences arise in scope and application. The WoS articles focus on specific, localized issues like emotion recognition and mental health detection, typically applying specialized models (eg, transformer-based models,⁸⁶ deep learning for schizophrenia detection⁸⁸*). In contrast, the SCOPUS abstracts explore broader, global problems such as multi-modal affective computing⁸⁹ and the cognitive impacts of COVID-19,⁹⁰ reflecting a wider international perspective. A unique aspect is the repetition of^86,87 and,⁸⁸ which are present in the ESCAP & ESCWA and all world but have different citation counts between the two databases, illustrating the varying academic reach across platforms.

Table 3.

The Most Global Cited Documents Group by Database and Region After 2022.

Database	Region	Ref	TC	Title/Description
WoS	ESCAP & ESCWA	⁸⁶*	49	Transformers for EEG-based emotion recognition: A hierarchical spatial information learning model
		⁸⁷*	38	A Temporal–Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals
		⁸⁸*	33	A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals
		⁹¹	33	Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood
		⁹²	32	A Matrix Determinant Feature Extraction Approach for Decoding Motor and Mental Imagery EEG in Subject Specific Tasks
		⁹³	31	A Review about Cognitive Workload Recognition Using EEG Signals and Machine Learning.
		⁹⁴	27	NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework
		⁹⁵	26	A Deep Neural Network for SSVEP-based BCIs
		⁹⁶	25	EEG-based emotion recognition in an immersive virtual reality environment: From local activity to brain network features
		⁹⁷	25	Spatial-temporal feature fusion neural network for EEG-based emotion recognition
Scopus	All World	⁸⁹	89	Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing
		⁹⁰	58	Cognitive, EEG, and MRI features of COVID-19 survivors: a 10-month study
		⁸⁶*	57	Transformers for EEG-based emotion recognition: A hierarchical spatial information learning model
		⁹⁸	47	The international cardiac arrest research consortium electroencephalography database
		⁸⁷*	42	A Temporal–Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals
		⁸⁸*	42	A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals
		⁹⁹	42	Tuning alpha rhythms to shape conscious visual perception
		¹⁰⁰	42	Delta-and theta-band cortical tracking and phase-amplitude coupling to sung speech by infants
		¹⁰¹	41	EEG-based measurement system for monitoring student engagement in learning 4.0
		¹⁰²	41	Glioblastoma remodelling of human neural circuits decreases survival

Abbreviations: TC (Total Citations), * indicate the three common articles in both databases.

One of the main direction for future research is the bibliometric analysis of some specific topics in these regions and its relations with EEGLAB and EEG such as Traditional Chinese Medicine like Herbs and Acupuncture^103,104 Islamic health-related topics,¹⁰⁵ and Yoga and meditation.¹⁰⁶ Other methodological direction is combing bibliometric analysis with functional data analysis such as Functioanl Clustering of knowledge-based system (KBS)¹⁰⁷ and Spatio-Temporal Dynamics of European Innovation.¹⁰⁸ The main limitation of this research is that it only considers the papers that cited the EEGLAB main paper, therefore many more articles are not considered and other databases such as DOAJ indexed-only or articles with other languages are not provided.

Supplemental Material

sj-docx-1-eeg-10.1177_15500594261428714 - Supplemental material for A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries

Supplemental material, sj-docx-1-eeg-10.1177_15500594261428714 for A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries by Mohammad Fayaz and Nadhim Azeez Sayel in Clinical EEG and Neuroscience

Supplemental Material

sj-docx-2-eeg-10.1177_15500594261428714 - Supplemental material for A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries

Supplemental material, sj-docx-2-eeg-10.1177_15500594261428714 for A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries by Mohammad Fayaz and Nadhim Azeez Sayel in Clinical EEG and Neuroscience

Footnotes

Acknowledgements

I would like to acknowledge that educational programs, such as organizing conferences, training workshops, and supporting researchers in these regions, play a crucial role in advancing research. For example, the 2021 Virtual EEGLAB Workshop Pacific/Asia was a significant event that contributed to this goal, with the support of Professors Arnaud Delorme, Scott Makeig, and Tzyy-Ping Jung (鍾子平) from the Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, USA, along with Professor Makoto Miyakoshi (宮腰誠) from the Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.

ORCID iD

Mohammad Fayaz

Ethical Considerations

There is no clinical trial or human subjects in this study.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets are available in request from authors.

Pre-Print Hosting

Preprint reference: Fayaz, M.; Azeez Sayel, N. How ASIA-PACIFIC and Arabian Countries Published Articles in the Scopus and WoS Indexed Sources with EEGLAB in 20 Years: A Bibliometric Study. Preprints 2024, 2024110750. https://doi.org/10.20944/preprints202411.0750.v1 Direct link: https://www.preprints.org/manuscript/202411.0750

Registered Clinical Trials (RCTs)

There is no clinical trial or human subjects in this study.

Supplemental Material

Supplemental material for this article is available online.

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