Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)

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Electroencephalogram (EEG) for Brain Disease Detection: A Bibliometric Analysis on 2013–2023 Research in the Scopus Database

Authors
Agus Muliantara1, *, Komang Dian Aditya Putra2
1Department of Informatics, Faculty of Mathematics and Natural Sciences, Udayana University, Badung, Bali, Indonesia
2Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Udayana University, Badung, Bali, Indonesia
*Corresponding author. Email: muliantara@unud.ac.id
Corresponding Author
Agus Muliantara
Available Online 13 May 2024.
DOI
10.2991/978-94-6463-413-6_11How to use a DOI?
Keywords
EEG; Brain; Disease; Bibliometric; Research; Database
Abstract

The diversity of brain disease can be attributed to the complexity of the nervous system. External, genetic, and epigenetic factors such as physical trauma, illness, and environmental factors can cause and worsen brain disease. The advancement of technology has led many to seek a lifestyle that is both health-conscious and easy. Biomedical research and treatment use several signals, including electroencephalograms. EEG captures spontaneous electrical activity in the cerebral cortex and can detect, monitor, and help brain disease patients. This study employs the Scopus database to conduct a bibliometric analysis of the EEG research for brain disease detection. The data was evaluated using the RStudio and VOSviewer applications. One hundred ninety-three papers from 2013 to 2023 were included in the final bibliometric dataset. China is known as the most impactful country, and Harvard Medical School is well recognized as a highly productive institution with significant global contributions. Wang J is recognized as the most prolific author. The article authored by Oh et al. in 2020 holds substantial influence in the field. The most frequently appearing keywords were electroencephalogram (173 occurrences with a link strength of 4740). These results are performed to provide a broad understanding of EEG research for brain disease detection.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
Series
Advances in Computer Science Research
Publication Date
13 May 2024
ISBN
978-94-6463-413-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-413-6_11How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Agus Muliantara
AU  - Komang Dian Aditya Putra
PY  - 2024
DA  - 2024/05/13
TI  - Electroencephalogram (EEG) for Brain Disease Detection: A Bibliometric Analysis on 2013–2023 Research in the Scopus Database
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 103
EP  - 118
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_11
DO  - 10.2991/978-94-6463-413-6_11
ID  - Muliantara2024
ER  -