Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

Adi Wijaya; Noor Akhmad Setiawan; Asma Hayati Ahmad; Rahimah Zakaria; Zahiruddin Othman; Adi Wijaya; Noor Akhmad Setiawan; Asma Hayati Ahmad; Rahimah Zakaria; Zahiruddin Othman

doi:10.3934/Neuroscience.2023012

AIMS Neuroscience

2023, Volume 10, Issue 2: 154-171. doi: 10.3934/Neuroscience.2023012

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Review

Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

1.
Department of Health Information Management, Universitas Indonesia Maju, Jakarta, Indonesia
2.
Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
3.
School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia

Received: 17 April 2023 Revised: 27 May 2023 Accepted: 12 June 2023 Published: 13 June 2023

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.
- electroencephalography,
- mild cognitive impairment,
- bibliometric analysis,
- scoping review,
- ScoRBA
Citation: Adi Wijaya, Noor Akhmad Setiawan, Asma Hayati Ahmad, Rahimah Zakaria, Zahiruddin Othman. Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)[J]. AIMS Neuroscience, 2023, 10(2): 154-171. doi: 10.3934/Neuroscience.2023012

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Abstract

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.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

Conceptualization, A.W.; data curation, N.A.S.; formal analysis, R.Z.; writing—original draft, Z.O.; writing—review & editing, A.H.A. All authors have read and agreed to the published version of the manuscript.

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