Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

Abstract

BackgroundWith the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.MethodsQEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.ResultsA total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.ConclusionThe present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

“…This review uses CiteSpace 5.8.R3 (64-bit) to accomplish visualization to obtain insights into the application of AI on EC and identify the research horizon and knowledge base of the field in large amounts of data. The most often employed metrics in bibliometric analysis are coauthorship, co-citation, and co-occurrence analysis (41)(42)(43). The purpose of co-authorship analysis is to examine the link between authors, nations, or organizations based on the number of articles produced together.…”

Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions

“…Bibliometric analyses have been useful in identifying key research trends and mapping the intellectual structure of neurofeedbackrelated research. For instance, Rong et al (2022) and Yao et al (2022) conducted bibliometric analyses on ASD and quantitative EEG research in neuropsychiatric disorders, revealing the most influential authors, institutions, and countries in the field as well as the most frequently studied brain regions and EEG features. These analyses shed light on the global research status and trends in autism spectrum disorder (ASD) and electroencephalogram (EEG), as well as how neurofeedback can be used as a treatment option, providing valuable insights for researchers and practitioners.…”

A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect

“…By applying signal processing methods to EEG signals, which are difficult to analyze, studies are carried out on the diagnosis of neurological diseases, including some feature extraction processes [5,7]. In addition to signal processing methods, it is possible to easily analyze EEG signals by applying machine learning (ML) or DL methods [8]. According to recent studies, when applying these methods, CAD systems are recommended.…”

“…According to recent studies, when applying these methods, CAD systems are recommended. In recent years, studies on neurological disorders have applied ML and DL methods together with signal processing methods [7][8][9][10].…”