1. Introduction [409 words]

2. Material and Methods [335 words]

3. Results (1958 Words)

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.

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.

3.7. 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.

4. Discussion (359 words)

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