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EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review
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 comm...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782188/ https://www.ncbi.nlm.nih.gov/pubmed/36548189 http://dx.doi.org/10.3390/neurolint14040084 |
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author | Tsiamalou, Athanasia Dardiotis, Efthimios Paterakis, Konstantinos Fotakopoulos, George Liampas, Ioannis Sgantzos, Markos Siokas, Vasileios Brotis, Alexandros G. |
author_facet | Tsiamalou, Athanasia Dardiotis, Efthimios Paterakis, Konstantinos Fotakopoulos, George Liampas, Ioannis Sgantzos, Markos Siokas, Vasileios Brotis, Alexandros G. |
author_sort | Tsiamalou, Athanasia |
collection | PubMed |
description | 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 “Brain-computer interfaces, a review” by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by “Brain-computer interfaces in neurological rehabilitation” 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–machine interface, motor imagery, and deep learning. Conclusions: EEG constitutes the most significant input in brain–computer 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. |
format | Online Article Text |
id | pubmed-9782188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97821882022-12-24 EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review Tsiamalou, Athanasia Dardiotis, Efthimios Paterakis, Konstantinos Fotakopoulos, George Liampas, Ioannis Sgantzos, Markos Siokas, Vasileios Brotis, Alexandros G. Neurol Int Review 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 “Brain-computer interfaces, a review” by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by “Brain-computer interfaces in neurological rehabilitation” 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–machine interface, motor imagery, and deep learning. Conclusions: EEG constitutes the most significant input in brain–computer 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. MDPI 2022-12-16 /pmc/articles/PMC9782188/ /pubmed/36548189 http://dx.doi.org/10.3390/neurolint14040084 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Tsiamalou, Athanasia Dardiotis, Efthimios Paterakis, Konstantinos Fotakopoulos, George Liampas, Ioannis Sgantzos, Markos Siokas, Vasileios Brotis, Alexandros G. EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review |
title | EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review |
title_full | EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review |
title_fullStr | EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review |
title_full_unstemmed | EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review |
title_short | EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review |
title_sort | eeg in neurorehabilitation: a bibliometric analysis and content review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782188/ https://www.ncbi.nlm.nih.gov/pubmed/36548189 http://dx.doi.org/10.3390/neurolint14040084 |
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