{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T04:12:40Z","timestamp":1793679160274,"version":"4.5.1"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neurology International"],"abstract":"<jats:p>Background: There is increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community. Methods: We performed an electronic search in Scopus, looking for studies reporting on rehabilitation in patients with neurological disabilities. We used the most influential papers to outline the knowledge base and carried out a word co-occurrence analysis to identify the research hotspots. We also used depicted collaboration networks between universities, authors, and countries after analyzing the cocitations. The results were presented in summary tables, plots, and maps. Finally, a content review based on the top-20 most cited articles completed our study. Results: Our current bibliometric study was based on 874 records from 420 sources. There was vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled \u201cBrain-computer interfaces, a review\u201d by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by \u201cBrain-computer interfaces in neurological rehabilitation\u201d by J. Daly and J.R. Wolpaw (708 citations). The US, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of functional magnetic imaging to EEG-based brain\u2013machine interface, motor imagery, and deep learning. Conclusions: EEG constitutes the most significant input in brain\u2013computer interfaces (BCIs) and can be successfully used in the neurorehabilitation of patients with stroke symptoms, amyotrophic lateral sclerosis, and traumatic brain and spinal injuries. EEG-based BCI facilitates the training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG-filtering algorithms.<\/jats:p>","DOI":"10.3390\/neurolint14040084","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T03:55:39Z","timestamp":1671162939000},"page":"1046-1061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Athanasia","family":"Tsiamalou","sequence":"first","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences, University of Thessaly, 41334 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Efthimios","family":"Dardiotis","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences, University of Thessaly, 41334 Larissa, Greece"},{"name":"Department of Neurology, General University Hospital of Larissa, 41110 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Paterakis","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences, University of Thessaly, 41334 Larissa, Greece"},{"name":"Department of Neurosurgery, General University Hospital of Larissa, 41110 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2491-2886","authenticated-orcid":false,"given":"George","family":"Fotakopoulos","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, General University Hospital of Larissa, 41110 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2958-5220","authenticated-orcid":false,"given":"Ioannis","family":"Liampas","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences, University of Thessaly, 41334 Larissa, Greece"},{"name":"Department of Neurology, General University Hospital of Larissa, 41110 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6337-4534","authenticated-orcid":false,"given":"Markos","family":"Sgantzos","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences, University of Thessaly, 41334 Larissa, Greece"},{"name":"Department of Neurology, General University Hospital of Larissa, 41110 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasileios","family":"Siokas","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences, University of Thessaly, 41334 Larissa, Greece"},{"name":"Department of Neurology, General University Hospital of Larissa, 41110 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6031-4528","authenticated-orcid":false,"given":"Alexandros G.","family":"Brotis","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, General University Hospital of Larissa, 41110 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/nrneurol.2010.201","article-title":"Neuroscience insights improve neurorehabilitation of poststroke aphasia","volume":"7","author":"Berthier","year":"2011","journal-title":"Nat. Rev. Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3389\/fnsys.2019.00074","article-title":"Principles of Neurorehabilitation After Stroke Based on Motor Learning and Brain Plasticity Mechanisms","volume":"13","author":"Maier","year":"2019","journal-title":"Front. Syst. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain Computer Interfaces, a Review","volume":"12","year":"2012","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1016\/S1474-4422(08)70223-0","article-title":"Brain-computer interfaces in neurological rehabilitation","volume":"7","author":"Daly","year":"2008","journal-title":"Lancet Neurol."},{"key":"ref_5","unstructured":"Tong, S., and Thakor, N.V. (2009). Quantitative EEG Analysis Methods and Clinical Applications, Artech House Series."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.joi.2017.08.007","article-title":"bibliometrix: An R-tool for comprehensive science mapping analysis","volume":"11","author":"Aria","year":"2017","journal-title":"J. Informetr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: VOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_8","unstructured":"(2021, September 10). Health Insurance Portability and Accountability Act of 1996 (HIPAA)|CDC 2019, Available online: https:\/\/www.cdc.gov\/phlp\/publications\/topic\/hipaa.html."},{"key":"ref_9","first-page":"e58494","article-title":"Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases","volume":"152","author":"AlRyalat","year":"2019","journal-title":"J. Vis. Exp. JoVE"},{"key":"ref_10","first-page":"338","article-title":"Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses","volume":"22","author":"Falagas","year":"2008","journal-title":"FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1212\/01.CPJ.0000437088.98407.fa","article-title":"Neurorehabilitation","volume":"3","author":"Barrett","year":"2013","journal-title":"Neurol. Clin Pract"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e82","DOI":"10.1016\/j.wneu.2019.05.226","article-title":"Predictors of Citations in Neurosurgical Research","volume":"130","author":"Oravec","year":"2019","journal-title":"World Neurosurg."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1590\/S0004-27492013000100008","article-title":"Influence of English language in the number of citations of articles published in Brazilian journals of ophthalmology","volume":"76","author":"Lira","year":"2013","journal-title":"Arq. Bras. Oftalmol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s40248-016-0081-0","article-title":"Bibliometric analysis of worldwide publications on multi-, extensively, and totally drug-resistant tuberculosis (2006\u20132015)","volume":"11","author":"Sweileh","year":"2017","journal-title":"Multidiscip. Respir. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/ana.23879","article-title":"Brain-machine interface in chronic stroke rehabilitation: A controlled study","volume":"74","author":"Broetz","year":"2013","journal-title":"Ann. Neurol."},{"key":"ref_16","first-page":"3","article-title":"fNIRS-based brain-computer interfaces: A review","volume":"9","author":"Naseer","year":"2015","journal-title":"Front. Hum. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TNSRE.2016.2521160","article-title":"State of the art and future directions for lower limb robotic exoskeletons","volume":"25","author":"Young","year":"2017","journal-title":"IEEE Trans. Neural. Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1038\/nrneurol.2016.113","article-title":"Brain-computer interfaces for communication and rehabilitation","volume":"12","author":"Chaudhary","year":"2016","journal-title":"Nat. Rev. Neurol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1177\/1545968306298934","article-title":"Origin of fatigue in multiple sclerosis: Review of the literature","volume":"22","author":"Kos","year":"2008","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/ncpneuro0990","article-title":"Mirror neurons and their clinical relevance","volume":"5","author":"Rizzolatti","year":"2009","journal-title":"Nat. Clin. Pract. Neurol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"30383","DOI":"10.1038\/srep30383","article-title":"Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients","volume":"6","author":"Donati","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.bspc.2016.09.007","article-title":"Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system","volume":"31","author":"Kevric","year":"2017","journal-title":"Biomed Signal Process Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.neuroimage.2012.08.019","article-title":"Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects","volume":"63","author":"Wagner","year":"2012","journal-title":"NeuroImage"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1113\/jphysiol.2006.123067","article-title":"Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation","volume":"579","author":"Dobkin","year":"2007","journal-title":"J. Physiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1152\/physrev.00027.2016","article-title":"Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation","volume":"97","author":"Lebedev","year":"2017","journal-title":"Physiol. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.nbd.2014.11.025","article-title":"Brain-machine interfaces in neurorehabilitation of stroke","volume":"83","author":"Soekadar","year":"2015","journal-title":"Neurobiol. Dis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3389\/fneng.2012.00013","article-title":"Detection of self-paced reaching movement intention from EEG signals","volume":"5","author":"Lew","year":"2012","journal-title":"Front Neuroengineering"},{"key":"ref_28","first-page":"129","article-title":"Reorganization of human cerebral cortex: The range of changes following use and injury","volume":"10","author":"Elbert","year":"2004","journal-title":"Neurosci Rev, J. Bringing Neurobiol. Neurol. Psychiatry"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.neuroimage.2013.03.045","article-title":"NIRS in clinical neurology\u2014A \u2018promising\u2019 tool?","volume":"85","author":"Obrig","year":"2014","journal-title":"NeuroImage"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/JPROC.2015.2415800","article-title":"Brain-computer interface for neurorehabilitation of upper limb after stroke","volume":"103","author":"Ang","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/j.1749-6632.2009.04580.x","article-title":"Neural reorganization underlies improvement in stroke-induced motor dysfunction by music-supported therapy","volume":"1169","author":"Schneider","year":"2009","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ramos-Murguialday, A., Sch\u00fcrholz, M., Caggiano, V., Wildgruber, M., Caria, A., Hammer, E.M., Halder, S., and Birbaumer, N. (2012). Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0047048"},{"key":"ref_33","first-page":"1435","article-title":"Uses of Bibliometric Techniques in Public Health Research","volume":"46","author":"Moura","year":"2017","journal-title":"Iran J. Public Health"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Benke, K., and Benke, G. (2018). Artificial Intelligence and Big Data in Public Health. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15122796"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1093\/jbcr\/iraa083","article-title":"Scientific Impact and Clinical Influence: Identifying Landmark Studies in Burns","volume":"41","author":"Ring","year":"2020","journal-title":"J. Burn Care Res. Off. Publ. Am. Burn Assoc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1177\/0312896219877678","article-title":"Conducting systematic literature reviews and bibliometric analyses","volume":"45","author":"Linnenluecke","year":"2020","journal-title":"Aust. J. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. Biomed Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105075","DOI":"10.1016\/j.cmpb.2019.105075","article-title":"Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017","volume":"183","author":"Shukla","year":"2020","journal-title":"Comput. Methods Programs Biomed"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0140-6736(13)61953-4","article-title":"Global and regional burden of stroke during 1990\u20132010: Findings from the Global Burden of Disease Study 2010","volume":"383","author":"Feigin","year":"2014","journal-title":"Lancet"},{"key":"ref_41","unstructured":"GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators (2019). Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol., 18, 56\u201387."}],"container-title":["Neurology International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2035-8377\/14\/4\/84\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:30Z","timestamp":1760146950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2035-8377\/14\/4\/84"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["neurolint14040084"],"URL":"https:\/\/doi.org\/10.3390\/neurolint14040084","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202211.0447.v1","asserted-by":"object"}]},"ISSN":["2035-8377"],"issn-type":[{"value":"2035-8377","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}