Academic Journal
Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)
| Title: | Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA) |
|---|---|
| Authors: | Adi Wijaya, Noor Akhmad Setiawan, Asma Hayati Ahmad, Rahimah Zakaria, Zahiruddin Othman |
| Source: | AIMS Neurosci AIMS Neuroscience, Vol 10, Iss 2, Pp 154-171 (2023) |
| Publication Status: | Preprint |
| Publisher Information: | American Institute of Mathematical Sciences (AIMS), 2023. |
| Publication Year: | 2023 |
| Subject Terms: | scorba, FOS: Computer and information sciences, Mild Cognitive Impairment, Cognitive Neuroscience, Neurosciences. Biological psychiatry. Neuropsychiatry, Machine Learning (stat.ML), Neuroimaging, Review, Analysis of Brain Functional Connectivity Networks, Epilepsy Detection, 03 medical and health sciences, EEG Analysis, mild cognitive impairment, bibliometric analysis, 0302 clinical medicine, Cognition, Statistics - Machine Learning, Health Sciences, Psychology, Deep Learning for EEG, Life Sciences, Electroencephalography, Brain-Computer Interfaces in Neuroscience and Medicine, Diagnosis and Management of Alzheimer's Disease, Computer science, 3. Good health, FOS: Psychology, Psychiatry and Mental health, Cognitive impairment, Quantitative electroencephalography, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Medicine, Neurons and Cognition (q-bio.NC), scoping review, electroencephalography, RC321-571, Neuroscience |
| Description: | Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field. |
| Document Type: | Article Other literature type |
| ISSN: | 2373-7972 |
| DOI: | 10.3934/neuroscience.2023012 |
| DOI: | 10.60692/ayher-97v79 |
| DOI: | 10.60692/2mgyh-kwq98 |
| DOI: | 10.48550/arxiv.2211.00302 |
| Access URL: | https://pubmed.ncbi.nlm.nih.gov/37426780 http://arxiv.org/abs/2211.00302 https://doaj.org/article/db50e682b1fd4ec6a61154e28c6fce7d |
| Rights: | CC BY SA URL: http://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0 (http://creativecommons.org/licenses/by/4.0/) ) |
| Accession Number: | edsair.doi.dedup.....b51632ed7d46a45659b17d96a7e9908f |
| Database: | OpenAIRE |
| ISSN: | 23737972 |
|---|---|
| DOI: | 10.3934/neuroscience.2023012 |