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Opportunities and Challenges of Multimodal Electroencephalography and Functional Near Infrared Spectroscopy in Neurological Disorders: A Bibliometric Analysis from 2005 to 2024

  • Nan Wang1,2,3,
  • Juanning Si4,
  • Yifang He4,
  • Sipeng Zhu1,
  • Xiaoke Chai1,3,
  • Tianqing Cao1,
  • Qiheng He1,
  • Yitong Jia1,
  • Yi Yang1,3,5,*  and
  • Jizong Zhao1,3,* 
Neurosurgical Subspecialties   2025;1(2):67-83

doi: 10.14218/NSSS.2025.00020

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Revised:

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Published online:

 Author information

Citation: Wang N, Si J, He Y, Zhu S, Chai X, Cao T, et al. Opportunities and Challenges of Multimodal Electroencephalography and Functional Near Infrared Spectroscopy in Neurological Disorders: A Bibliometric Analysis from 2005 to 2024. Neurosurgical Subspecialties. 2025;1(2):67-83. doi: 10.14218/NSSS.2025.00020.

Abstract

Background and objectives

Multimodal applications combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in cognitive neuroscience and have progressively been applied to clinical applications, such as the joint diagnosis of amyotrophic lateral sclerosis, Alzheimer’s disease, and pediatric epilepsy. This study conducted a bibliometric analysis of EEG-fNIRS synchronization techniques over the past 20 years. The aim was to clarify their diagnostic and therapeutic value in clinical applications, particularly in the neurological system, and to guide future research and development trends.

Methods

This study utilized the Web of Science Core Collection database to analyze documents published between January 1, 2005, and May 13, 2024. CiteSpace and VOSviewer were employed for visual analyses of co-author relationships, keywords, citation patterns, and journal distributions. By overlaying dual-map diagrams and analyzing annual publication trends, the study identified research hotspots, development trends, and the evolution of EEG-fNIRS technology.

Results

A total of 645 articles and reviews from 55 countries were analyzed. The USA contributed the most publications. The team led by Michela Balconi at the Catholic University of the Sacred Heart published the highest number of papers. Frontiers in Human Neuroscience had the greatest number of publications, while NeuroImage had the highest citation impact. Recent research has primarily focused on the application of neuroimaging and neurophysiological techniques (e.g., EEG, fNIRS, functional magnetic resonance imaging), brain activation, and brain-computer interface.

Conclusions

This study highlights the applications and developmental trends of dual-modality EEG-fNIRS technology. Specifically, this approach can assist in diagnosing neurological disorders, assessing activation and connectivity within functional brain regions, and evaluating therapeutic neuromodulation in clinical neurology. Overall, multimodal fusion is poised to advance neuroscience research significantly.

Keywords

Electroencephalogram, Functional near-infrared spectroscopy, Bibliometric analysis, CiteSpace, VOSviewer, Nervous system diseases, Brain-computer interface, Hotspot

Introduction

Electroencephalography (EEG) is a widely used technique for monitoring brain electrical activity. Advances in high-density EEG systems and advanced statistical methods have significantly enhanced its spatial resolution.1,2 Evoked potentials obtained from EEG are crucial for evaluating the brain’s response to sensory stimuli, offering key insights into neurological function. Consequently, EEG plays a pivotal role in diagnosing and monitoring a broad range of neurological disorders.3,4

The functional near-infrared spectroscopy (fNIRS) assesses changes in oxyhemoglobin and deoxyhemoglobin concentrations to infer neuronal activity.5–8 This technique provides valuable insights into functional brain connectivity and activation patterns during cognitive tasks. Its ability to capture cognitive, visual, and auditory signals expands the applicability of brain-computer interfaces (BCIs), particularly benefiting stroke patients through personalized therapeutic interventions.9 While fNIRS has gained attention for its capacity to measure hemodynamic responses, similar to functional magnetic resonance imaging, it is limited by relatively low spatial and depth resolution.10,11

EEG is characterized by high temporal resolution but limited spatial resolution, whereas fNIRS offers better spatial resolution with lower temporal precision. To address these individual limitations, the integration of EEG and fNIRS, often termed photoelectric synchronization, has been applied in research and clinical practices due to their complementary strengths. By combining electrical and hemodynamic data, this multimodal approach offers a more comprehensive understanding of brain function.12,13 Studies have demonstrated the unique value of EEG-fNIRS integration in exploring functional connectivity during resting and sleep states, investigating the neural mechanisms of emotional interaction, and analyzing complex neural activity patterns during motor execution, observation, and imagery.14,15 This synergistic approach enables high-resolution analysis of cortical activation patterns, facilitating the detection of subtle brain activity and connectivity features. It has significantly advanced our understanding of the neurophysiological mechanisms underlying motor and neurological disorders, providing robust support for diagnosis and therapeutic interventions. For instance, EEG-fNIRS integration has proven effective in mechanistic studies of mitochondrial epilepsy.16

The application of photoelectric synchronization technology extends further—to detecting brain activity in infants and children, assessing residual consciousness in patients with brain injuries, and advancing emotion recognition models in human-computer interaction. These applications highlight the potential of photoelectric synchronization as a frontier in neuroscience and clinical research. Despite its growing relevance, comprehensive evaluations of the progress and future directions in this field remain limited. Bibliometric analysis, a quantitative method for evaluating scientific literature, attempts to identify patterns, trends, and relationships within a body of research. This approach provides valuable insights into the evolution and structure of a particular scientific domain. This bibliometric analysis aimed to comprehensively examine the EEG and fNIRS research literature from 2005 to 2024. By evaluating integrated EEG-fNIRS studies, this work sought to identify emerging research trends, highlight pivotal findings, delineate clinical applications, and provide insights to inform and guide future research directions.

Materials and methods

Data sources and retrieval methods

As of May 13, 2024, a systematic search was conducted in the Web of Science Core Collection (WoSCC) database using the following topic search terms: (EEG OR electroencephalogram OR electroencephalography OR electroencephalograph OR electroencephalograms OR “brain mapping” OR encephalogram OR “brain electric activity mapping” OR “brain electric” OR “cerebral electric” OR “brain electrical” OR “cerebral electrical” OR “brain potentials” OR “electroencephalography brain”) AND (fNIRS OR “functional near-infrared spectroscopy” OR “functional near-infrared reflectance spectroscopy”). The search was restricted to articles and reviews published in English. All retrieved records and relevant publications were saved in plain text format (.txt) for subsequent analysis, including detailed reference documentation. To identify eligible studies, the titles and abstracts of all retrieved documents were independently screened by a team of authors. Any discrepancies during the screening process were resolved through discussion and consensus. The search strategy and inclusion criteria were developed based on guidelines and recommendations provided by the China Knowledge Translation Assistant.

Software tools and data visualization

A total of 645 publications related to EEG and fNIRS, published between 2005 and 2024, were analyzed using CiteSpace (version 6.2.R3 (64-bit) Beta Advanced) and VOSviewer (version 1.6.20).17 The geographic distribution and publication output by country were visualized using Tableau Public Desktop (64-bit-2021-3-3). CiteSpace was employed for collaborative network analyses involving countries, institutions, and authors, as well as for keyword co-occurrence and document co-citation analyses.18,19 VOSviewer was utilized to perform co-authorship and co-occurrence analyses, leveraging its embedded clustering algorithms to reveal collaboration networks among authors and institutions.20,21 The tool also facilitated advanced analyses, including the exploration of associations between keywords via co-occurrence analysis and the use of a temporal overlay function to visualize dynamic changes in the network over time.22,23 Data output from CiteSpace and VOSviewer were exported to Microsoft Excel for further classification, filtering, and refinement. To standardize keywords, synonymous terms were merged, duplicates were removed, and irrelevant entries (e.g., numbers) were excluded to ensure accuracy and consistency in the final analysis. A summary of the literature screening process and analytical workflow is presented in the flowchart (Fig. 1).

Flow chart.
Fig. 1  Flow chart.

Illustration of the process of reference inclusion: peer-reviewed articles and reviews published in English. Our initial search retrieved 686 publications, of which 645 met all eligibility requirements for final analysis. EEG, electroencephalography; fNIRS, functional near-infrared spectroscopy.

Results

General analysis

Based on the established search parameters, a total of 645 publications related to EEG-fNIRS were retrieved from the WoSCC for the period 2005–2024. These works originated from 55 countries or regions across Asia, Europe, and the Americas, involving 307 institutions, 481 authors, and 90 research categories, and were published in 213 journals. Regarding publication numbers, a clear upward trend in global research output is evident, with the annual number of publications increasing from a single paper (0.31%) in 2005 to 97 papers (15.03%) in 2023 (Fig. 2a). Concerning publication and citation trends in EEG and fNIRS literature, the overall pattern shows continuous growth from 2004 to 2023. Notably, the number of publications has increased significantly since 2015, rising from about 100 in 2015 to 228 in 2023, indicating that research activity in this field has intensified annually in recent years. The cumulative number of citations grew exponentially, reaching 23,866 in 2023 (Fig. 2b). This trend signifies growing scholarly interest in EEG-fNIRS research over recent years.

Distribution of publications from 2005 to 2024.
Fig. 2  Distribution of publications from 2005 to 2024.

(a) Blue bars: annual publications; yellow curve: accumulated publications. The left axis shows data for accumulated publications, and the right axis shows data for annual publications. (b) Citation trends from 2005 to 2024. Blue bars: number of annual paper citations; yellow curve: cumulative number of paper citations. The left axis shows data on the cumulative number of citations to papers, and the right axis shows data on the annual number of citations to papers.

Analysis of publications, citations, and collaborations by countries or regions

We conducted a detailed analysis of contributions from 55 countries or regions engaged in photoelectric synchronization research. Figure 3a depicts a collaboration map, where the size of each circle represents the number of publications from each country or region. The top ten countries by publication count are: the United States in first place with 186 papers (28.84% of the total), followed by China with 139 papers (21.56%), and Germany with 80 papers (12.40%). Other major contributors include Korea (10.39%), Italy (9.61%), the United Kingdom (9.15%), Canada (8.06%), Japan (5.74%), France (4.03%), and Australia (2.95%). Countries ranked 10th and below have published fewer than 20 papers (Table 1). Of particular note is the consistent growth in research output from the United States between 2015 and 2023 (excluding 2018), and the exponential growth from China between 2016 and 2023. In contrast, both Germany and South Korea have experienced stable but declining trends in annual publication numbers since 2017 (Fig. 3b). We analyzed co-authorship among 27 countries that have each published at least five papers in this field. In the collaboration network, node size indicates the number of publications per country, while line thickness reflects the level of cooperation between countries. The top five countries with the strongest overall collaborative strength are the United States (142), Germany (74), China (69), the United Kingdom (50), and Italy (36) (Fig. 3c). In terms of citation count, the United States leads with 6,519 citations, followed by Italy (3,509), Germany (2,930), South Korea (2,712), China (2,115), Japan (2,108), the United Kingdom (1,389), and Canada (1,003) (Table 1). Overall, the relatively low publication output from individual countries and the limited international collaboration suggests significant potential to strengthen partnerships in this field. Enhancing such collaborations could greatly contribute to the development and advancement of EEG-fNIRS research across different nations.

Countries or regions contributing to EEG and fNIRS research.
Fig. 3  Countries or regions contributing to EEG and fNIRS research.

(a) 55 countries or regions that published articles. (b) Plot showing the change in the trend of articles published in the top four countries. (c) Plot showing the coverage of countries or regions that published more than five papers. The closer the color is to red, the more recent the year of collaboration; the closer the color is to blue, the earlier the year of collaboration. EEG, electroencephalography; fNIRS, functional near-infrared spectroscopy.

Table 1

Top 10 countries or regions in terms of the number of articles

RankCountries/RegionsCount (n)Percentage (%)CentralityYearTotal number of citationsAverage number of citationsTotal link strength
1USA18628.840.3220056,51935.05142
2China13921.550.1920102,11515.2269
3Germany8012.400.0620082,93036.6374
4South Korea6710.390.0820102,71240.4835
5Italy629.610.0720093,50956.6036
6England599.150.4620081,38923.5450
7Canada528.060.1120091,00319.2935
8Japan375.740.1420052,10856.9729
9France264.030.08201589334.3535
10Australia192.950.02201636919.4219

Analysis of institutional output and collaboration

The analysis of institutional collaborations reveals that a total of 307 institutions have contributed to EEG and fNIRS research. The top 10 institutions by publication count and related metrics were analyzed in detail (Table 2). Université de Montréal ranked first with 27 articles (4.19%), followed by the Catholic University of the Sacred Heart (3.26%), Pusan National University (3.10%), Beijing Normal University (2.95%), and the University of London (2.79%). The collaboration network diagram illustrates relationships among these 307 institutions. Each node represents an institution, with node size proportional to the number of publications. Larger nodes indicate higher research output, while node color reflects publication year. Université de Montréal occupies a central position in the network, with the highest number of connections, underscoring its role as a key hub for collaboration. Other high-output institutions, such as the Catholic University of the Sacred Heart, Pusan National University, and Beijing Normal University, also exhibit significant collaborative ties (Fig. 4a). For further analysis, 62 institutions with five or more publications were selected for co-authorship network analysis. The top five institutions by total link strength were Université de Montréal (22), University of Houston (15), McGill University (15), Drexel University (14), and University of Leipzig (14). The diagram demonstrates active international collaboration, particularly among leading universities in Europe, North America, and Asia. For example, Université de Montréal has established close collaborative links with McGill University, University of Houston, and University of London. Notably, these collaborations span multiple regions, highlighting the globalized nature of EEG and fNIRS research (Fig. 4b).

Table 2

The top 10 institutions contributing to publications in EEG and fNIRS

RankInstitutionsCount (n)Percentage (%)CentralityYear
1Universite de Montreal274.190.12011
2Catholic University of the Sacred Heart213.2602015
3Pusan National University203.100.12010
4Beijing Normal University192.950.12010
5University of London182.790.12008
6University of Houston172.640.082013
7Eberhard Karls University of Tubingen294.500.032015
8Harvard University162.480.142011
9Drexel University142.170.112005
10Polytechnique Montreal121.8602011
Institutional contributions to EEG and fNIRS research.
Fig. 4  Institutional contributions to EEG and fNIRS research.

(a) Co-occurrence networks of institutions. (b) Network of institutions with ≥ five publications: co-authorship analysis. EEG, electroencephalography; fNIRS, functional near-infrared spectroscopy.

Analysis of author influence and collaboration

Table 3 highlights the top 16 authors and their respective H-indices, ranked by the number of articles they have contributed. Among them, Michela Balconi emerges as the most prolific researcher, with 21 articles, accounting for 3.26% of the total publications. Following closely are Hong Keum-Shik (2.33%), Pouliot Philippe (1.55%), Zhang Yingchun (1.40%), and Rossi Sonja (1.40%), as illustrated in Figure 5a. We analyzed the collaboration network of 50 authors who have co-authored more than five papers (Fig. 5b). The strength of collaboration among authors is indicated by the thickness of the connections between nodes. The authors with the highest total connection strength are Pouliot Philippe (54), Lesage Frederic (50), Vannasing Phetsamone (46), Tremblay Julie (45), and Dang Khoa Nguyen (40).

Table 3

Top 16 authors with the most articles about EEG and fNIRS

RankAuthorsCountH-indexInstitution
1Balconi, Michela2138Catholic University of the Sacred Heart
2Hong, Keum-Shik1554Pusan National University
3Pouliot, Philippe1025Universite de Montreal
4Zhang, Yingchun915Guangdong Prov Work Injury Rehabil Hosp
5Rossi, Sonja913Medical University of Innsbruck
6Vanutelli, Maria Elide917University of Milan
7Lesage, Frederic845Polytechnique Montreal
8Li, Rihui724University of Macau
9Ayaz, Hasan737University of Pennsylvania
10Lassonde, Maryse653Universite de Montreal
11Wallois, Fabrice630Institut National de la Sante et de la Recherche Medicale
12Dang Khoa Nguyen620Universite de Montreal
13Vannasing, Phetsamone620Sainte Justine Univ Hosp Ctr
14Tremblay, Julie617Sainte Justine Univ Hosp Ctr
15Al-Shargie, Fares613Abu Dhabi University
16Naseer, Noman621Air University Islamabad
Authors contributing to EEG and fNIRS research.
Fig. 5  Authors contributing to EEG and fNIRS research.

(a) Author coupling network analysis. (b) Top 10 authors with the largest number of publications. (c) Network map of authors who were co-cited in more than five publications. (d) Author co-citation analysis. Each node represents a cited author, and each link between two nodes represents a co-citation relationship. The size of each node represents the number of citations for that author. EEG, electroencephalography; fNIRS, functional near-infrared spectroscopy.

An author’s influence in a scientific field depends more on the number of citations. Coupled network analysis shows that the most cited author in this field is Hong Keum-Shik (1,615 citations), followed by Dan Ippeita (715 citations), Tsuzuki Daisuke (671 citations), Ayaz Hasan (627 citations), and Khan M. Jawad (579 citations) (Fig. 5c). A co-citation network was constructed to analyze intellectual relationships among highly cited authors (Fig. 5d). Six authors exceeded 100 co-citations: ANONYMOUS (149), SCHOLKMANN F (134), HUPPERT T (126), JCUI X (122), FERRARI M (113), and STRANGMAN G (102). Node size corresponds to citation count, while the thickness of connecting lines indicate co-citation frequency, mapping conceptual linkages within the field. Node color represents publication year, with warmer hues denoting more recent work.

Analysis of journals and categories

A total of 645 articles were published across 213 journals. The top 10 journals with the most EEG and fNIRS articles and the top 10 most cited journals are listed in Table 4. Frontiers in Human Neuroscience accounted for the highest number of publications (48 articles, 7.44% of all articles), followed by Frontiers in Neuroscience (5.11%), Scientific Reports (4.03%), Neuroimage (3.88%), and IEEE Access (3.88%). In the co-citation analysis, 394 journals were identified as being co-cited in more than 50 publications. Neuroimage had the largest number of citations (595), followed by Front Hum Neurosci (435), PLOS One (394), Hum Brain Mapp (334), and Clin Neurophysiol (307).

Table 4

Top 10 popular journals and cited journals

RankPopular journalsRecords (n)2024 impact factor2024 JCR PartitionCited journalsCitations2024 impact factor2024 JCR Partition
1Frontiers In Human Neuroscience482.9Q3Neuroimage5955.7Q1
2Frontiers In Neuroscience334.3Q2Front Hum Neurosci4352.9Q3
3Scientific Reports264.6Q2Plos One3943.7Q2
4Neuroimage255.7Q1Hum Brain Mapp3344.8Q1
5Ieee Access253.9Q2Clin Neurophysiol3074.7Q1
6Sensors233.9Q2Scientific Reports2854.6Q2
7Neurophotonics215.3Q1Front Neurosci-Switz2744.3Q2
8Brain Sciences203.3Q3P Natl Acad Sci Usa27311.1Q1
9Ieee Transactions On Neural Systems184.9Q2J Neurosci Meth2543Q3
10Plos One183.7Q2J Neurosci2485.3Q1

A total of 90 research areas are represented in the co-occurrence analysis of Web of Science categories. The most well-represented research area is Neurosciences, comprising 316 records (48.992% of all articles), followed by Biomedical Engineering (74 records, 11.473%), Electrical and Electronic Engineering (70, 10.853%), Psychology (62, 9.612%), and Radiology, Nuclear Medicine, and Medical Imaging (60, 9.302%) (Table 5). This indicates that the EEG-fNIRS dual-modality technique is a multifaceted and multidisciplinary field. Analyzing journals and research areas provides insight into the publications in the field of EEG and fNIRS research, highlighting important journals and major research domains. Researchers can use this information to explore existing literature and identify potential collaboration opportunities.

Table 5

Top 10 well-represented research areas

RankResearch areasRecord (n)% (of 654)
1Neurosciences31648.992
2Engineering biomedical7411.473
3Engineering electrical electronic7010.853
4Psychology629.612
5Radiology nuclear medicine medical imaging609.302
6Multidisciplinary sciences517.907
7Optics497.597
8Neuroimaging436.667
9Computer science information systems385.891
10Instruments instrumentation355.426

Analysis of references and bursts in citations

Based on the citation analysis, 106 documents were cited more than 20 times. The collaboration network of these 106 documents is revealed (Fig. 6a). The top ten documents with the highest citation counts are listed in Table 6. Hong et al.24 conducted a review of a framework for brain therapy and BCI for individuals with locked-in syndrome, utilizing a hybrid multimodality approach combining EEG and fNIRS. The integration of multiple modalities in brain imaging and prosthesis control represents a novel advancement in the field.25 Furthermore, the authors introduced a hybrid EEG-fNIRS scheme aimed at decoding eight distinct brain commands from the frontal brain region for BCI applications.26

Citation and co-citation analyses.
Fig. 6  Citation and co-citation analyses.

(a) Network map of documents with more than 20 citations. (b) Literature clustering graph. (c) Literature timeline graph. (d) Network map of documents co-cited in more than 50 publications. (e) The top 21 references with robust citation bursts.

Table 6

Top 10 citation analysis of documents on EEG and fNIRS research

RankTitleFirst authorCorresponding AuthorSourcePublication yearCitations (n)
1Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfacesHong, KSHong, MJFrontiers in Human Neuroscience201831
2Hybrid brain-computer interface techniques for improved classification accuracy and increased number of commands: a reviewHong, KSKhan, MJFrontiers in Neurorobotics201729
3Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter ControlKhan, MJHong, KSFrontiers in Neurorobotics201721
4Early detection of hemodynamic responses using EEG: a hybrid EEG-fNIRS studyKhan, MJHong, KSFrontiers in Human Neuroscience201818
5A brain-computer interface based on a few-channel EEG-fNIRS bimodal systemGe, SWang, HIEEE Access201717
6Hybrid EEG-fNIRS bei fusion using multi-resolution singular value decomposition (MSVD)Khan, MUHasan, MAHFrontiers in Human Neuroscience202017
7Enhancing classification performance of fNIRS-BCI by identifying cortically active channels using the z-score methodNazeer, HAyaz, YSensors202016
8Improvement of information transfer rates using a hybrid EEG-NIRS brain-computer interface with a short trial length: offline and pseudo-online analysesShin, JHwang, HJSensors201815
9Enhancing the performance of a hybrid EEG-fNIRS system using channel selection and early temporal featuresLi, RZhang, YFrontiers in Human Neuroscience201715
10Shining a light on awareness: a review of functional near-infrared spectroscopy for prolonged disorders of consciousnessRupawala, MCruse, DFrontiers in Neurology201815

EEG, electroencephalography; fNIRS, functional near-infrared spectroscopy.

Using CiteSpace’s log-likelihood ratio algorithm, the 10 largest clusters were identified by consensus (Fig. 6b). Based on cluster size (number of documents), the 10 largest clusters are labeled: “information transfer rate”, “machine learning”, “using functional near-infrared spectroscopy”, “human epilepsy”, “arithmetic task”, “Stroop task”, “social interaction”, “I social neuroscience”, “binge-eating disorder”, and “walking intervention”. The top-ranked item by citation bursts is Pinti P in Cluster #5.27 The second is Naseer Noma in Cluster #0,28 followed by Khan MJ in Cluster #0,26 Hong KS in Cluster #4,24 and Buccino AP in Cluster #0 (Fig. 6c).29

We utilized VOSviewer to comprehensively analyze all cited references. The co-citation network shown in Figure 6d includes publications with co-citation frequencies exceeding 50. The thickness of connecting lines reflects the strength of co-citation links between references. Among the most frequently cited works, Fazli et al.,30 published in NeuroImage, ranks highest, with a link strength of 972 and a citation frequency of 92. Following closely is the paper by Scholkmann et al,31 also in NeuroImage, with a link strength of 861 and citation frequency of 93. Notably, Ferrari et al.,32 published in NeuroImage, made a significant impact, achieving the highest citation frequency of 103 and a link strength of 806.

Figure 6e illustrates the top 21 most cited references. The dark blue line indicates the citation duration from 2005 to 2024, while the red line indicates the range of mutations in citation duration. The reference with the highest number of citations and citation burst value is the article entitled fNIRS-based BCIs by Noman Naseer, with a citation burst from 2015 to 2020 of 13.69.28 The second most cited reference is by Fazli et al.,30 entitled Enhanced performance by a hybrid EEG-fNIRS-BCI, with a citation burst of 13.26.

Analysis of keywords and topics

Figure 7a demonstrates the top 11 keywords with the strongest citation bursts. In total, 538 keywords were collected (Fig. 7b). The five most frequently occurring keywords were: NIRS (239 occurrences), fNIRS (229), EEG (197), functional magnetic resonance imaging (94), and brain (85). A total of 118 author keywords appearing more than five times were analyzed (Fig. 8a). Figure 8b maps these keywords, with colors indicating average publication years; most keywords were published after 2019, indicated by greener colors. Following clustering of the cited networks, 18 clusters were obtained and labeled with title words or keywords from citing articles (Fig. 8c). Density visualization in Figure 8d shows the same keywords mapped by frequency.

Keyword co-occurrence and ranking.
Fig. 7  Keyword co-occurrence and ranking.

(a) The top 11 bursts of keywords analyzed by CiteSpace; “strength” represents the intensity of the burst, “begin” represents the starting year of the burst, “end” indicates the ending year of the burst; the red dotted line indicates the duration of the burst. The blue line indicates the entire period from 2005 to 2024. (b) Keywords: co-word network and clustering in the field of EEG and fNIRS. Node size reflects co-word frequency; links represent co-word intensity; different colors represent clustering results of the co-word network; closer relationships share the same color. EEG, electroencephalography; fNIRS, functional near-infrared spectroscopy.

Co-occurrence analysis of keywords.
Fig. 8  Co-occurrence analysis of keywords.

(a) Mapping of keywords from studies. (b) Distribution of keywords according to average publication year (blue: earlier; red: later). (c) Each node represents a keyword; links between nodes represent co-citation intensity; different colors represent different clusters. (d) Distribution of keywords according to mean frequency of appearance. Keywords in yellow occurred with the highest frequency. (e) The largest 10 clusters. (f) High-frequency topic keywords from 2005 to 2024.

Using CiteSpace’s log-likelihood ratio algorithm, the 10 largest clusters were identified by consensus (Fig. 8e). Along the timeline, nodes of different colors indicate references from different years within clusters, while nodes farther to the right indicate more recent references. The modularity Q value (0.9448) was greater than 0.3, and the average silhouette score (0.9605) was greater than 0.7, indicating that the clusters are convincing and well-structured. The five largest clusters were labeled as “motor imagery”, “brain mapping”, “optical topography”, “neurovascular coupling”, and “medical signal processing”. Examining the timing of keyword appearance allows us to understand the development of these fields over time and estimate recent trends and directions. Figure 8f shows the high-frequency subject word map from 2005 to 2024. The research focus has gradually shifted from disease diagnosis toward treatment research. Attention should be paid to recent hotspots such as “task analysis”, “feature extraction”, “deep learning”, and “coherence”.

Discussion

This study conducted a bibliometric analysis of literature extracted from public databases to identify research hotspots and future development directions in EEG and fNIRS. Notably, a significant rise in publication activity has been observed since 2020, likely driven by advances in technologies such as BCI and artificial intelligence, which have positioned EEG-fNIRS as a compelling research topic. Despite growing attention from the scientific community, substantial potential remains for further expansion in this field, particularly in the context of photoelectric synchronization. The observed trends suggest increasing interest, but the current scale of research indicates considerable room for development and broader exploration.

Additionally, we analyzed the authorship of articles published in the WoSCC and found that the USA dominates the application of EEG and fNIRS. Interdisciplinary and international collaborations play a crucial role in advancing synchronous research in photonics. Université de Montréal is the most active institution, focusing its research on applying EEG-fNIRS to stroke and epilepsy monitoring.33 In particular, it has played an important role in understanding the pathophysiology of temporal lobe epilepsy, revealing complex local and distal oxygenation changes during temporal lobe seizures and the non-linear hemodynamic response in refractory focal epilepsy in humans.34,35 The University of Montréal exhibits the highest intermediary centrality and connection strength, boasting the strongest connections to other institutions and the highest number of collaborative publications. Following closely are the University of Eberhard Karls in Tübingen, Germany, and Drexel University in the USA, ranked second and third in the collaboration analysis, respectively. Pusan National University, meanwhile, achieved the highest average citation count in this study.

Michela Balconi is the most active researcher in this field. As evidenced by 21 co-authored publications from 2015 to 2024, her research primarily focuses on detecting frontal cortex responses to emotions via EEG and fNIRS under different environmental conditions,36 such as resting state,37,38 COVID-19,39 painful stimuli,40 audiovisual stimuli,14 and interoceptive attentiveness.41,42 Additionally, Balconi et al.43 used EEG and fNIRS to reveal a facilitatory role for motor imagery and executive motion sensation, suggesting that readiness potential amplitude acts as a predictor of hemodynamic brain activity, modulated by task and gesture type factors. Recent research has focused on EEG-fNIRS multimodal hyperscanning techniques in social interactions, including cooperative and competitive relationships,44–47 as well as interpersonal relationships,48 highlighting brain network connectivity in neuroscience as a current hotspot and cutting-edge direction for the field.49 Keum-Shik Hong of Pusan National University, together with researchers from the University of Wisconsin-Madison and Sejong University, has made significant contributions to optoelectronic synchronization research from an EEG-fNIRS-BCI perspective.25,50 Their work involves detecting brain signals during various tasks, assessing activation of specific brain regions, and achieving precise classification of brain signals through multimodal techniques. Research in this area has advanced the development of wearable BCIs.28,51,52 Philippe Pouliot has also significantly contributed to advancing optoelectronic synchronization research from an EEG-fNIRS-BCI perspective. As a Professor of Neuroscience at the University of Montréal, his primary focus has been on enhancing algorithms for NIRS technology to improve visualization of functional brain connectivity.53,54

Clinical applications of EEG-fNIRS

According to keyword and theme analyses, Alzheimer’s disease, epilepsy,55 attention-deficit hyperactivity disorder, stroke,56 consciousness disorders, and Parkinson’s disease are among the most prevalent clinical applications of EEG-fNIRS.57 The technique was initially used to study cognitive processes such as attention, working memory, target categorization, problem-solving, and human waste processing.58 In recent years, it has been extensively applied in cognitive neurosciences,59 neurological disorders,60 psychiatric disorders,61 mental health, pediatrics,62 sociology,63 and psychology. This section focuses on the use of EEG-fNIRS techniques in clinical disorders.

EEG dynamically monitors brain activity through electrical signals with high temporal resolution, while fNIRS detects cerebral blood oxygenation changes using near-infrared light, offering high spatial resolution. These complementary, non-interfering techniques provide a more comprehensive view of brain function. Jindal et al.64 used fNIRS-EEG integration to study changes in brain activity during stroke recovery. They found that changes in rSO2 were negatively correlated with EEG power changes following electrical stimulation, indicating that decreased EEG power corresponded to increased corticospinal excitability. This study expands the application of fNIRS-EEG in assessing brain activity patterns in stroke patients and provides a quantitative method for evaluating therapeutic interventions.64 Similarly, Li et al.13 developed a multimodal neuroimaging technique combining EEG and fNIRS to assess motor deficits post-stroke. In their study involving 18 stroke patients and nine healthy controls, they demonstrated reduced task-induced activity in the somatosensory cortex of stroke patients and highlighted how improved functional connectivity in motor-related areas correlated with motor recovery during rehabilitation. Baseline connectivity patterns also predicted recovery outcomes, emphasizing the potential of fNIRS-EEG integration in monitoring and predicting rehabilitation success.13 Overall, fNIRS-EEG integration leverages the advantages of high temporal and spatial resolution, offering new insights into neurovascular coupling, brain network plasticity, and motor recovery in stroke rehabilitation. This multimodal approach not only enhances understanding of brain activity mechanisms but also promotes precise clinical applications and rehabilitation strategies.

Li and colleagues integrated EEG and fNIRS data with machine learning techniques to enhance the precision of depression diagnosis.65 They collected resting-state EEG signals and hemodynamic data from the forehead of 25 patients diagnosed with depression and 30 healthy individuals. By leveraging a support vector machine model, they achieved a classification accuracy of 92.7% using hybrid EEG-fNIRS features, compared to 81.8% with EEG data alone. Key biomarkers distinguishing depressive states included enhanced local efficiency in the delta band, hemispheric asymmetry in the theta band, and brain oxygen entropy.65 These findings underscore the potential of integrating EEG and fNIRS with machine learning as a reliable, non-invasive tool for individual-level depression diagnosis, offering both spatial and temporal insights into underlying neural mechanisms.

The integration of EEG and fNIRS has also shown increasing relevance in epilepsy research. Nourhashemi et al.66 employed this multimodal approach to investigate 25 episodes of childhood absence seizures in eight pediatric patients. Approximately 20 seconds before the onset of spike-wave discharges, transient shifts in direct current potentials were detected, correlating with preictal hemodynamic changes in cerebral blood flow and hemoglobin levels. These findings revealed intricate neurovascular interactions preceding seizures, providing a deeper understanding of mechanisms underlying absence epilepsy.66 Peng et al.67 further explored the role of EEG-fNIRS integration in epilepsy, focusing on both focal seizures and interictal epileptiform discharges. Their review highlighted the potential of these technologies to evaluate hemodynamic and neuronal changes associated with epileptic events, improving the localization and lateralization of seizure foci, particularly in focal epilepsy. Specific patterns of cerebral blood volume and oxygenation changes were associated with various seizure types, such as temporal and frontal lobe epilepsy. Notably, fNIRS could detect pre-seizure hemodynamic alterations, offering new insights into seizure initiation.67 Collectively, these studies demonstrate the potential of EEG-fNIRS integration as a robust tool for understanding the neurovascular mechanisms of epilepsy and advancing diagnostic and therapeutic strategies. Future research should prioritize expanding the clinical applicability of this multimodal approach.

Strengths and weaknesses of the study

This research conducted a comprehensive search and analysis of literature encompassing various terms related to EEG and fNIRS. The analysis utilized two distinct econometric software tools, potentially introducing bias due to variations in calculation methods between the software. Given that scalp EEG predominantly captures cortical signals perpendicular to the recording electrodes, while fNIRS detects hemodynamic changes through oxy/deoxyhemoglobin concentration variations to indirectly reflect neural activity, the clinical implementation of EEG-fNIRS integration remains largely confined to research settings. This approach currently offers limited practical utility for clinicians engaged in the non-invasive diagnosis and treatment of brain disorders.

This study represents the first bibliometric analysis to systematically document and evaluate clinically treated EEG and fNIRS. An extensive literature review was conducted, though some limitations exist. Primarily, the search and analysis relied predominantly on the WoSCC core database, neglecting other databases such as PubMed and Scopus, potentially overlooking pertinent literature. Nevertheless, it is important to acknowledge that WoSCC is a widely utilized database in scientometrics, with most bibliometric software compatible with its format. Furthermore, incomplete keyword extraction may have impacted the outcomes of keyword analysis.

Future research directions

Challenges persist in optoelectronic synchronization, including limited spatial resolution and the inability to capture subcortical data. To address these limitations, potential strategies include incorporating supplementary sensors, integrating complementary modalities such as EEG, and augmenting fNIRS optoelectronic probes. Additionally, further optimization of signal processing and algorithms for EEG-fNIRS applications is needed.5,6,68 A significant additional limitation pertains to the temporal characteristics of the signals. EEG signals exhibit high temporal resolution, enabling analysis of both spectral and temporal dynamics. In contrast, fNIRS signals reflect slower hemodynamic changes, introducing an inherent physiological latency relative to the underlying neural activity.69,70 Therefore, achieving precise temporal synchronization (neurovascular coupling) between the fast electrical events measured by EEG and the slower hemodynamic responses measured by fNIRS presents a critical methodological challenge.

Wearable, integrated EEG-fNIRS technology represents a critical advancement in this field. Supporting large channel counts, extensive dynamic ranges, and rapid data acquisition and transmission requires enhanced data interfaces and control schemes. Furthermore, dry EEG electrodes with high gain and input impedance may be the optimal choice for extended periodic monitoring.12 The integration of EEG and fNIRS technologies faces inherent technical limitations. Although commercial devices capable of simultaneous EEG-fNIRS recording are available, achieving spatially co-localized measurements of neuronal activity remains challenging. This difficulty arises primarily from fundamental differences in signal acquisition mechanisms and the physical constraints of the hardware. EEG detects electrical potentials generated by neuronal populations, with relatively small electrodes achieving good contact with the scalp surface.71 In contrast, fNIRS measures hemodynamic responses by detecting changes in near-infrared light absorption between optical sources (emitters) and detectors (optodes). These optodes possess a finite size and require specific spacing to measure hemodynamic responses within underlying tissue. Consequently, to attempt spatial correspondence (co-channel configuration), EEG electrodes must be placed within the gaps between fNIRS optodes. This placement inevitably compromises spatial coverage and the fidelity of EEG signal acquisition due to physical obstruction and suboptimal electrode positioning. These spatial constraints also hinder comprehensive coverage of the entire brain region with truly co-localized EEG-fNIRS channels.72

BCI research frequently utilizes integrated EEG-fNIRS systems to enhance the amount of accessible brain information and improve classification accuracy.73,74 Non-invasive BCIs have demonstrated potential in stroke rehabilitation and are commonly applied in motor, sensory, and cognitive functions; however, limited spatial resolution remains a recognized constraint.75 Moreover, the utilization of non-invasive BCIs has been found to augment the quantity of brain-derived information.

Conclusions

Undoubtedly, EEG-fNIRS multimodal technology will become an important imaging technique for disease diagnosis in clinical settings, useful for guiding treatment. The purpose of this study was to provide an overview of global publications using bibliometric methods and visualization tools to uncover changes and developments in the field over recent decades. Optically synchronized multimodal fusion is valuable as a non-invasive, non-implantable examination in clinical BCI applications involving cross-fertilization of multiple disciplines. The Global Brain Initiative, proposed in 2023, outlines future directions in neurology. Cyberneuroscience is a multidisciplinary clinical collaboration that requires further exploration and mastery. Moving forward, research should prioritize examining the human brain, task analysis, feature extraction, and brain-camera interface challenges. It is essential to adopt a multidisciplinary approach and leverage multi-omics technology to facilitate collaboration among research groups across disciplines. Ultimately, the goal is to identify additional prognostic tools for neurological diseases through both clinical and basic research.

Declarations

Acknowledgement

None.

Data sharing statement

All data supporting the findings in this article are available within the article. Researchers can access the data by applying to the Web of Science Core Collection database and the Google Scholar database.

Funding

This work was supported by the Beijing Natural Science Foundation Project (7232049, 7252004); International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project (Z221100002722014); 2022 Open Project of Key Laboratory and Engineering Technology Research Center in the Rehabilitation Field of the Ministry of Civil Affairs (2022GKZS0003); Chinese Institute for Brain Research Youth Scholar Program (2022-NKX-XM-02); Science and Technology Innovation 2030 (2022ZD0205300); The Key Collaborative Research Program of the Alliance of International Science Organizations (ANSO-CR-KP-2022-10); and CAMS Innovation Fund for Medical Sciences (CIFMS, 2019-I2M-5-021).

Conflict of interest

The authors declare no conflict of interest. The research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors’ contributions

Study conception, drafting of the manuscript, figure design (NW, JS), literature review (NW, JS, XC, TC), editing (YH, SZ, XC, TC), revision (YH, SZ), providing feedback on clinical implications, data interpretation (QH, YJ), study supervision and guidance, review, and approval of the final version (YY, JZ).

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Wang N, Si J, He Y, Zhu S, Chai X, Cao T, et al. Opportunities and Challenges of Multimodal Electroencephalography and Functional Near Infrared Spectroscopy in Neurological Disorders: A Bibliometric Analysis from 2005 to 2024. Neurosurgical Subspecialties. 2025;1(2):67-83. doi: 10.14218/NSSS.2025.00020.
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Received Revised Accepted Published
April 30, 2025 June 16, 2025 June 20, 2025 June 30, 2025
DOI http://dx.doi.org/10.14218/NSSS.2025.00020
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