Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

The Bibliometric Analysis of EEGLAB Software in the Web of Science Indexed Articles

Version 1 : Received: 13 October 2023 / Approved: 16 October 2023 / Online: 16 October 2023 (10:36:07 CEST)

A peer-reviewed article of this Preprint also exists.

Fayaz, M. The Bibliometric Analysis of EEGLAB Software in the Web of Science Indexed Articles. Neuroscience Informatics 2023, 100154, doi:10.1016/j.neuri.2023.100154. Fayaz, M. The Bibliometric Analysis of EEGLAB Software in the Web of Science Indexed Articles. Neuroscience Informatics 2023, 100154, doi:10.1016/j.neuri.2023.100154.

Abstract

Introduction: EEGLAB is one of the most famous software for processing, analyzing, and researching experiments that have Electroencephalography (EEG) datasets. Due to the numerous and famous add-ins along with global, widespread communications and online free training, its popularity increased every year. Method: To address this phenomenon from a bibliographic perspective, we found 20,464 citations in Google Scholar for the main EEGLAB reference since 8/27/2023. Then, only the Web of Science (WOS) articles were 12,700 that they were extracted. The results were analyzed with Bibliometrix package from CRAN R software. Results: The time span of these articles is from 2004 to 2023 with 12,700 documents in 1,125 sources (journals, books, etc.), 29,125 authors, 19,062 author’s keywords, 13,707 keywords PLUS, 279,617 references. The annual growth rate is 28.12 %, international Co-authorship is 37.27 % and Co-authors per document is s 4.89 and the average citations per document is 22.51. The most relevant sources are Neuroimage, Frontiers in Human Neurosciences, Scientific Reports, Psychophysiology, and PLOS One with 780, 526, 446,425, and 371 articles, respectively. The most cited countries are the USA, Germany, and the United Kingdom with 93,093, 32,621, and 20,748 total citations, respectively. The ERPLAB, ADJUST, and ICLabel add-ins have the local to global citation ratios equal to 85.4%, 65.1%, and 78.2% respectively. Other bibliometric analyses such as co-occurrence networks and thematic maps of abstracts, titles, and keywords are estimated and presented. Conclusions: EEGLAB is among the most cited MATLAB toolboxes in computational neuroscience. Many developed and developing countries use it in their research publications.

Keywords

EEGLAB; bibliometric analysis; computational neurosciences; MATLAB; R

Subject

Medicine and Pharmacology, Neuroscience and Neurology

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