How ASIA-PACIFIC and Arabian Countries Published Articles in the Scopus and WoS Indexed Sources with EEGLAB in 20 Years: A Bibliometric Study

1. Introduction [409 words]

After the development of the electroencephalogram (EEG) by Hans Berger in 1929 and since 1936 it became a major diagnostic tool and instrument in neuroscience globally (Keshavan et al., 2024; Mecarelli, 2019; Pavlov et al., 2021). From the 1990s many different academic software applications were developed for brain mapping with EEG (Baillet et al., 2011) such as EEGLAB (Delorme & Makeig, 2004) , Field-Trip (Oostenveld et al., 2011) , SPM (Litvak et al., 2011) and recent advances in Python such as MNE (Gramfort et al., 2014). The EEGLAB is an open source application developed by a team with the leading of Professor Arnaud Delorme and Scott Makeig from Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, USA. (Delorme & Makeig, 2004) The main features are providing Independent Component Analysis (ICA) and time/frequency analysis, various methods for artifact removal, a user-friendly graphical user interface (GUI), multiformat data importing, and interactive plotting. It has been downloaded about 350,000 times more than 15,265 researchers in the EEGLAB news list and more than 160 plug-ins (EEGLAB Website).

More than 12,700 Web of Science indexed research articles and documents cited EEGLAB papers worldwide with an estimated international Co-authorship is 37.27% (Fayaz, 2023). As stated in many bibliometric analysis, for example in Construction Health and Safety Management (Ding et al., 2023), Visualizations in Computer Graphics (Sajovic & Boh Podgornik, 2022) , Brain-Computer Interface (Stegman et al., 2020) and Good Scientific Practice (Niso et al., 2022) , EEGLAB is the main software.

The bibliometric study of geographically specific regions is of interest in neuroscience such as : neuroscience research in Saudi Arabia (Alhibshi et al., 2020), Turkey (Kocak et al., 2019), Iran (Ashrafi et al., 2012), Latin America (Forero et al., 2020), Brazil (Hoppen & Vanz, 2016), Neurosurgical Research in Southeast Asia (Omar II et al., 2022), Egyptian neurosurgical publications (Azab & Salem, 2022), Neurodegenerative Disorders in Arab Countries (El Masri et al., 2021) and Alzheimer's Disease in the world and China (Dong et al., 2019). Usually, the most cited articles came from the United States (US) and North America, European countries, etc., and the remaining parts of the world were not well-studied (Fayaz, 2023). However, there is no bibliometric analysis for EEGLAB paper citations in specific regions such as Asia-Pacific and Arabian states with 80 countries and territories. In this study, the SCOPUS and WOS databases were analyzed for these regions with bibliometrix R packages (Aria & Cuccurullo, 2017).

2. Material and Methods [335 words]

2.1. 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.(Regional Commissions) They are including:

• United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) with 58 countries and territories (Division, 2024). 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.

• The United Nations Economic and Social Commission for West Asia (ESCWA) or Arab States with the following 22 countries and territories (United Nations Economic and Social Commission for Western Asia, 2024):

  I.

  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.

2.2. 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. (Delorme & Makeig, 2004) The date of searching and downloading is 03/28/2024.

2.3. Statistical and Data Analysis

The bibliometric analysis methods are used with bibliometrix package (Aria & Cuccurullo, 2017) in POSIT (https://posit.co/) and R software (https://www.r-project.org/)(R, 2023).

3. Results (1958 Words)

3.1. The Descriptive Statistics

In Google Scholar, there exits 22,298 citations for EEGLAB software (until 03/28/2024) (Delorme & Makeig, 2004), meanwhile this number is reduced into 14,958 ($\approx$67.1%) citations in the WoS and 15,827 ($\approx$70.1%) citations in the Scopus, respectively. The accessible WoS results include 14,598 citations. The number of total citations in WoS for all selected regions is 4,836 ($\approx$21.6%) articles and for ENEA, NCA, PACIFIC, SEA, SSWA and Arab states were 3,168 ($\approx$14.2%), 172 ($\approx$0.77%) , 836 ($\approx$3.75%), 276 ($\approx$1.23%), 564 ($\approx$2.52%) and 155 ($\approx$0.69%) citations respectively. These citations were published from 2004 to 2024 in 975 sources (journals, conference proceedings, etc.), written by 12,140 authors and they cited 134,022 unique references. The annual growth rate (considering the first three months of 2024) is estimated at 20.85%, the international co-authorship is 42.39%, and the average citation per doc is 13.97 documents. According to the Table 1, the ENEA subregion is in one cluster and other subregions are in another cluster based on the k-mean clustering with a single linkage.

The list of citations grouped by country name and region is presented in Supplementary materials , Table S1 for both WoS and Scopus databases. According to this table, China (2,257), Australia (755), Japan (574), the Republic of Korea (392), India (231), Iran (191), the Russian Federation (163), Singapore (151), New Zealand (103), both Malaysia (79) and Türkiye (79) are top-ten countries according to the WoS citations. The difference between the number of citations in WoS and Scopus for each country is calculated as $Diff=\#WoS-\#Scopus$. Hong Kong (China), India, Japan, China, Russian Federation, Iran and Macao (China) have $Diff\ge 40$ citations. Hence, further analysis such as the country-specific profile based on Scopus is provided in ">Supplementary materials, Part B Country Profile based on Scopus citations.

3.2. Lotka's law coefficient estimation for Author Scientific Productivity

The Lotka's law coefficient,”the frequency of publication by authors in any given field as an inverse square law, where the number of authors publishing a certain number of articles is a fixed ratio to the number of authors publishing a single article”, is estimated with lotka() function in bibliometrix package (Aria & Cuccurullo, 2017; Lotka, 1926) for author scientific productivity in each region. (Table 2) The goodness of fit ,R²,of all models are more than 0.95 and the p-value of the two-sample Kolmogorov-Smirnov test between the empirical and the theorical Lotka's Law distribution (Beta=2) are higher than 0.05, and they are not statistically different.

To compare the Lotka curves between regions, the two groups are created: 1) ENEA, PACIFIC and SEA for East Asia regions and 2) NCA, SSWA and Arab States for central and west Asia regions. The lotka curves are compared with an ANOVA test for functional data with fanova.onefactor () function in fda.usc package.(Cuevas et al., 2004; Febrero-Bande & De La Fuente, 2012) The P-value based on the 1000 bootstrap resamples for number and proportion of authors are estimated 0.174 and 0.226 , respectively. Therefore, there is no statistical difference between these two regions. (Supplementary materials, Part C)

3.3. Bradford Law

The core sources based on Bradford's law are estimated with bradford() function for all six regions: 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 presented separately in the Supplementary materials, Part D.

3.4. The Most Global Cited Documents

Table 3 presented the two most globally cited documents for each region group by all times and after 2020 based on SCOPUS database. Their total citation (TC) frequency and average per year are provided. Some authors have more than one affiliation from different countries or their affiliation is changed. Therefore, only the mentioned affiliation of the published papers is considered in Table 3.

Table 3. The Most Global Cited Documents Group by Region and Period based on SCOPUS.

Region	Period	Ref	TC		Article Title/Description
			#	per Year
ENEA
	All Time	(Bin et al., 2009)	558	34.88	Online Multi-Channel Steady-State Visual Evoked Potential (SSVEP)-based BCI with Canonical Correlation Analysis (CCA)
		(Norton et al., 2015)	270	27.00	Online Multi-View Transfer Takagi-Sugeno-Kang (TSK) fuzzy system for estimating EEG-Based Driver Drowsiness
	≥2020	(Jiang et al., 2020)	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
		(Jin et al., 2020)	85	17.00	Bispectrum based channel selection (BCS) method for motor imagery (MI)-based BCI
NCA
	All Time	(G. Knyazev et al., 2009)	169	10.56	Synchronization Of Event-Related Delta and Theta In Explicit And Implicit Emotion Processing
		(Knyazev et al., 2011)	162	11.57	Relations between Heart–brain interactions and somatosensory perception and evoked potentials
	≥2020	(Al et al., 2020)	104	20.80	Independent Component Analysis (ICA) in default mode network and EEG alpha oscillations
		(Jin et al., 2020)	85	17.00	Bispectrum based channel selection (BCS) method for motor imagery (MI)-based BCI
PACIFIC
	All Time	(Whitham et al., 2007)	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.
		(Badcock et al., 2013)	229	19.08	Validation of the Emotiv EPOC® EEG Gaming System for Measuring Research Quality Auditory for Auditory Event-Related Potentials (ERPs)
	≥2020	(Jiang et al., 2020)	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
		(Klug & Gramann, 2021)	80	20.00	The ICA method improves the quality of EEG mobile and stationary experiments
SEA
	All Time	(Norton et al., 2015)	270	27.00	Online Multi-View Transfer Takagi-Sugeno-Kang (TSK) fuzzy system for estimating EEG-Based Driver Drowsiness
		(Islam et al., 2016)	219	24.33	A Review about Detection and Removal Artifacts in Scalp EEG
	≥2020	(Fahimi et al., 2020)	77	19.25	Deep Convolutional Generative Adversarial Networks (DCGANS) for Generating Artificial EEG in BCI
		(Jeong et al., 2020)	70	14.00	Decoding Movement-Related Cortical Potentials (MRCP) based Brain-Machine Interface (BMI)
SSWA
	All Time	(Acar et al., 2011)	397	28.36	Scalable Tensor Factorizations, CANDECOMP/PARAFAC (CP), with Missing Data
		(Islam et al., 2016)	219	24.33	A Review about Detection and Removal Artifacts in Scalp EEG
	≥2020	(Hosseini et al., 2020)	113	28.25	A review of Machine Learning Methods for EEG signal Processing
		(Rossini et al., 2020)	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)
Arab States
	All Time	(Arnal et al., 2015)	177	17.70	EEG Delta–Beta Coupled Oscillations Underlie Temporal Prediction Accuracy
		(Alhanbali et al., 2019)	143	10.21	A Trial about Listening Effort with capturing EEG
	≥2020	(Čukić et al., 2020)	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.
		(Islam et al., 2020)	25	5.00	EEG mobility artifact removal for ambulatory epileptic seizure prediction applications
All Six Regions
	All Time	(Bin et al., 2009)	558	34.88	Online Multi-Channel Steady-State Visual Evoked Potential (SSVEP)-based BCI with Canonical Correlation Analysis (CCA)
		(Acar et al., 2011)	397	28.36	Scalable Tensor Factorizations, CANDECOMP/PARAFAC (CP), with Missing Data
	≥2020	(Jiang et al., 2020)	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
		(Hosseini et al., 2020)	113	28.25	A review of Machine Learning Methods for EEG signal Processing

Abbreviations: TC (Total Citations).

Table 3. The Most Global Cited Documents Group by Region and Period based on SCOPUS.

Region	Period	Ref	TC		Article Title/Description
			#	per Year
ENEA
	All Time	(Bin et al., 2009)	558	34.88	Online Multi-Channel Steady-State Visual Evoked Potential (SSVEP)-based BCI with Canonical Correlation Analysis (CCA)
		(Norton et al., 2015)	270	27.00	Online Multi-View Transfer Takagi-Sugeno-Kang (TSK) fuzzy system for estimating EEG-Based Driver Drowsiness
	≥2020	(Jiang et al., 2020)	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
		(Jin et al., 2020)	85	17.00	Bispectrum based channel selection (BCS) method for motor imagery (MI)-based BCI
NCA
	All Time	(G. Knyazev et al., 2009)	169	10.56	Synchronization Of Event-Related Delta and Theta In Explicit And Implicit Emotion Processing
		(Knyazev et al., 2011)	162	11.57	Relations between Heart–brain interactions and somatosensory perception and evoked potentials
	≥2020	(Al et al., 2020)	104	20.80	Independent Component Analysis (ICA) in default mode network and EEG alpha oscillations
		(Jin et al., 2020)	85	17.00	Bispectrum based channel selection (BCS) method for motor imagery (MI)-based BCI
PACIFIC
	All Time	(Whitham et al., 2007)	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.
		(Badcock et al., 2013)	229	19.08	Validation of the Emotiv EPOC® EEG Gaming System for Measuring Research Quality Auditory for Auditory Event-Related Potentials (ERPs)
	≥2020	(Jiang et al., 2020)	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
		(Klug & Gramann, 2021)	80	20.00	The ICA method improves the quality of EEG mobile and stationary experiments
SEA
	All Time	(Norton et al., 2015)	270	27.00	Online Multi-View Transfer Takagi-Sugeno-Kang (TSK) fuzzy system for estimating EEG-Based Driver Drowsiness
		(Islam et al., 2016)	219	24.33	A Review about Detection and Removal Artifacts in Scalp EEG
	≥2020	(Fahimi et al., 2020)	77	19.25	Deep Convolutional Generative Adversarial Networks (DCGANS) for Generating Artificial EEG in BCI
		(Jeong et al., 2020)	70	14.00	Decoding Movement-Related Cortical Potentials (MRCP) based Brain-Machine Interface (BMI)
SSWA
	All Time	(Acar et al., 2011)	397	28.36	Scalable Tensor Factorizations, CANDECOMP/PARAFAC (CP), with Missing Data
		(Islam et al., 2016)	219	24.33	A Review about Detection and Removal Artifacts in Scalp EEG
	≥2020	(Hosseini et al., 2020)	113	28.25	A review of Machine Learning Methods for EEG signal Processing
		(Rossini et al., 2020)	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)
Arab States
	All Time	(Arnal et al., 2015)	177	17.70	EEG Delta–Beta Coupled Oscillations Underlie Temporal Prediction Accuracy
		(Alhanbali et al., 2019)	143	10.21	A Trial about Listening Effort with capturing EEG
	≥2020	(Čukić et al., 2020)	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.
		(Islam et al., 2020)	25	5.00	EEG mobility artifact removal for ambulatory epileptic seizure prediction applications
All Six Regions
	All Time	(Bin et al., 2009)	558	34.88	Online Multi-Channel Steady-State Visual Evoked Potential (SSVEP)-based BCI with Canonical Correlation Analysis (CCA)
		(Acar et al., 2011)	397	28.36	Scalable Tensor Factorizations, CANDECOMP/PARAFAC (CP), with Missing Data
	≥2020	(Jiang et al., 2020)	120	30.00	Soft, curved electrode systems capable of integration on the auricle for a persistent BCI
		(Hosseini et al., 2020)	113	28.25	A review of Machine Learning Methods for EEG signal Processing

Abbreviations: TC (Total Citations).

Table 4 compares the top 10 articles with the highest number of globally total citations after 2022 between ESCAP and ESCWA regions in WOS database and all countries in the world in SCOPUS.

3.5. The Co-Occurrence Network of WoS subject

The co-occurrence network of WoS subjects classification for all six regions is presented in Figure 1. (Supplementary materials, Part E provides a separate analysis for each region)

3.6. The Co-Citation Network Papers

The co-citation network papers for all six regions are presented in Figure 2. The papers are : (Oldfield, 1971), (Makeig, 1993), (Bell & Sejnowski, 1995), (Benjamini & Hochberg, 1995), (Makeig & Jung, 1996), (Brainard & Vision, 1997), (Pfurtscheller & Da Silva, 1999), (Klimesch, 1999), (Lachaux et al., 1999), (Jung et al., 2000), (Pascual-Marqui, 2002), (Wolpaw et al., 2002), (Polich, 2007), (Maris & Oostenveld, 2007), (Faul et al., 2007), (Klimesch et al., 2007), (Rubinov & Sporns, 2010), (Tadel et al., 2011), (Mognon et al., 2011), (Oostenveld et al., 2011), (Klimesch, 2012), (Lopez-Calderon & Luck, 2014), (Luck, 2014) and (Pion-Tonachini et al., 2019). (Supplementary materials, Part F provides a separate analysis for each region)

3.7. The Historiography

The historiography (historical co-citation network) are produced with histNetwork() and histPlot(). It has node (document cited by other documents) and the edge (direct citation) with the first author name and publishing year. (Ciavolino et al., 2022) The plots are presented in a layout with 6 plots for each subregions and one plot for all six subregions together in Figure 3.

In the ENEA subregion, there exists two clusters: The first one starts at 2010 “A novel approach for enhancing the signal-to-noise ratio and detecting automatically event-related potentials (ERPs) in single trials” (Hu et al., 2010) and at 2012 “Gamma-band oscillations in the primary somatosensory cortex—a direct and obligatory correlate of subjective pain intensity” (Zhang et al., 2012) and continues to the 2014 article “Single-trial time–frequency analysis of electrocortical signals: Baseline correction and beyond” (Li Hu et al., 2014) and it ended at 2019 article “Neural indicators of perceptual variability of pain across species” (Hu & Iannetti, 2019). The second cluster has two articles in 2017: “Temporal dynamics of reward anticipation in the human brain” (Zhang et al., 2017) and “Reward processing in gain versus loss context: An ERP study.” (Zheng et al., 2017)

In the NCA region, there is one cluster: it starts with a 2008 article “Anxiety and oscillatory responses to emotional facial expressions” (Knyazev, Bocharov, et al., 2008) and 2011 article “The default mode network and EEG alpha oscillations: an independent component analysis” (Knyazev et al., 2011) and endet at 2016 article “Anxiety, depression, and oscillatory dynamics in a social interaction model” (Knyazev et al., 2016).

In the PACIFIC region, there exists two clusters: In the first cluster, the 2008 article “Long-interval cortical inhibition from the dorsolateral prefrontal cortex: a TMS–EEG study” (Daskalakis et al., 2008) and 2009 article “Suppression of γ-oscillations in the dorsolateral prefrontal cortex following long interval cortical inhibition: a TMS–EEG study” (Farzan et al., 2009) are main references, in the middle there are two articles, the 2014 article “Removing artefacts from TMS-EEG recordings using independent component analysis: importance for assessing prefrontal and motor cortex network properties” (Rogasch et al., 2014) and the 2015 article “Cortical inhibition of distinct mechanisms in the dorsolateral prefrontal cortex is related to working memory performance: A TMS–EEG study” (Rogasch et al., 2015) and it ended with 2019 article “Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials” (Biabani et al., 2019) and 2020 article “Source-based artifact-rejection techniques available in TESA, an open-source TMS–EEG toolbox” (Mutanen et al., 2020). In the second cluster: there exits two articles about Emotiv EPOC (Badcock et al., 2013; Badcock et al., 2015).

In the SEA region, there exists three clusters, The first cluster started at 2014 article “Hybrid fNIRS-EEG based classification of auditory and visual perception processes” (Putze et al., 2014) . The second cluster started at 2014 articles “Discriminative analysis of brain functional connectivity patterns for mental fatigue classification” (Sun, Lim, Meng, et al., 2014) and “Functional cortical connectivity analysis of mental fatigue unmasks hemispheric asymmetry and changes in small-world networks” (Sun, Lim, Kwok, et al., 2014) , in the middle is the 2018 article “Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks” (Dimitrakopoulos et al., 2018) and it ended with 2020 article “Dynamic reorganization of functional connectivity unmasks fatigue related performance declines in simulated driving” (Wang et al., 2020). The third cluster start with 2017 article “Dynamic functional segregation and integration in human brain network during complex tasks” (Ren et al., 2016) and it ended with 2020 article “Towards machine to brain interfaces: sensory stimulation enhances sensorimotor dynamic functional connectivity in upper limb amputees.” (Ding et al., 2020).

In the SSWA region, there exits six clusters. The first cluster started with the 2013 article “Sensitive periods for the functional specialization of the neural system for human face processing” (Röder et al., 2013) and ended with two 2018 articles “Evidence of a retinotopic organization of early visual cortex but impaired extrastriate processing in sight recovery individuals” and “Motion processing after sight restoration: No competition between visual recovery and auditory compensation” (Bottari et al., 2018; Sourav et al., 2018). In the second cluster, the 2015 article “Minimum connected component–a novel approach to detection of cognitive load induced changes in functional brain networks” (Vijayalakshmi et al., 2015) exit. The third cluster is the cluster of Bahar Güntekin articles with some reviews (Gülmen Yener et al., 2013; Güntekin & Başar, 2014, 2016; Güntekin et al., 2019). The fourth cluster is Hamid Karimi-Rouzbahani articles(Karimi-Rouzbahani et al., 2017a, 2017b). The fifth cluster has two articles about automated detection of schizophrenia with EEG(Jahmunah et al., 2019; Shalbaf et al., 2020). The sixth cluster is Fatemeh Hasanzadeh articles (Hasanzadeh et al., 2019, 2020).

In the Arab states, there exits five clusters. In the first cluster there exited an 2011 article “A comparison of methods for separation of transient and oscillatory signals in EEG” (Jmail et al., 2011). In the second cluster a 2015 article, “EEGNET: An open source tool for analyzing and visualizing M/EEG connectome” (Hassan et al., 2015) exited. In the third cluster, Fares Al-Shargie articles exited (Al-Shargie et al., 2019; Alex et al., 2020; Yahya et al., 2019). In the fourth cluster, the 2017 article “Eeg-based brain-computer interface for decoding motor imagery tasks within the same hand using choi-williams time-frequency distribution” existed (Alazrai et al., 2017). The fifth cluster for 2020 article “Eeg-based neurohaptics research: A literature review” (Alsuradi et al., 2020) existed.

In these six regions overall, there exits three clusters. In the first cluster it started with a 2008 article “Long-interval cortical inhibition from the dorsolateral prefrontal cortex: a TMS–EEG study” (Daskalakis et al., 2008) and it reached 2014 article “Removing artefacts from TMS-EEG recordings using independent component analysis: importance for assessing prefrontal and motor cortex network properties” (Rogasch et al., 2014) and finally it reached the 2019 article “Clinical utility and prospective of TMS–EEG” (Tremblay et al., 2019). The second cluster have two articles from Gennady G Knyazev (G. Knyazev et al., 2009; Knyazev, Bocharov, et al., 2008) and an article from Bahar Güntekin (Güntekin & Başar, 2014). The third cluster contains 4 articles. It started with 2012 article “Gamma-band oscillations in the primary somatosensory cortex—a direct and obligatory correlate of subjective pain intensity” (Zhang et al., 2012) and three articles from Li Hu (Hu & Iannetti, 2019; Hu et al., 2013; Li Hu et al., 2014).

Figure 3. The historiography (historical co-citation network) plots, (a) ENEA, (b) NCA, (c) PACIFIC, (d) SEA, (e) SSWA, (f) Arab Sates and (g) All six subregions together.

Figure 3. The historiography (historical co-citation network) plots, (a) ENEA, (b) NCA, (c) PACIFIC, (d) SEA, (e) SSWA, (f) Arab Sates and (g) All six subregions together.

3.8. The Further Analysis

The further analysis grouped by subregions such as : Collaboration Network Between Institute, Country Collaboration Network, Authors Production Over Time and Keyword Plus Trend Topic are in the Supplementary materials in Part G, H, I and J, respectively.

4. Discussion (359 words)

In the year of the 20th anniversary of EEGLAB (2004 through 2023), EEGLAB Newsletter issue 17 in February 2024 (Newsletter, 2024) and a bibliometric analysis of all 12,700 WoS citations of EEGLAB software until 8/27/2023 studied this software. (Fayaz, 2023) This new updated analysis with all 15,827 Scopus and WOS instead of only WOS citations of EEGLAB until 03/28/2024 showed that they were published in 2,072 sources, written by 33,070 authors, cited 763,160 references, and presented 21,384 keywords. And the annual growth rate is 18.63% and the international co-authorship is 34.8%.

In addition, the comparison between all citations of all World and ESCAP and ESCWA regions together showed that (Fayaz, 2023), about 35% of all WoS documents and 41% of authors came from these two regions while the average citation per doc in the world is doubled these two regions. Educational programs such as organizing conferences and training workshops, and supporting researchers in these regions (subregions) can be the basis for faster development and better quality of research. For example, A Virtual EEGLAB Workshop Pacific/Asia in 2021 was held with the hosting of Professor Tzyy-Ping Jung (鍾子平) from the Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, USA and Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan and Professor Makoto Miyakoshi (宮腰誠) from Division of Child and Adolescent Psychiatry, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio and Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, Ohio (2021 Virtual EEGLAB Workshop Pacific/Asia, 2021)

One of the main direction for future research is 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 (Lim et al., 2021; Zhao et al., 2022) Islamic health-related topics (Kannan et al., 2022), and Yoga and meditation (Gaur et al., 2020). 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.