Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

• Published in: Frontiers in neuroscience Vol. 17; p. 1128851
• Main Authors: Liu, Mingrui, Liu, Baohu, Ye, Zelin, Wu, Dongyu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 20.03.2023; Frontiers Media S.A
• Subjects: Alzheimer's disease; Bibliometrics; Citation indexes; CiteSpace; Cluster analysis; Co authorship; Cocitation; Cognitive ability; Dementia; Diagnosis; EEG; electroencephalogram; Electroencephalography; Keywords; mild cognitive impairment; Neural networks; Neurodegenerative diseases; Neurological diseases; Neuroscience; Software; Trends; United Kingdom > UK; United States > US; Italy; CiteSpace; mild cognitive impairment; Alzheimer’s disease; bibliometrics; electroencephalogram
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2023.1128851

Electroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI. Related publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses. Between 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were "EEG," "mild cognitive impairment," and "Alzheimer's disease". The application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.