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

TL;DR

This paper reviews a decade of EEG research related to mild cognitive impairment, highlighting key themes like ERPs, QEEG, and machine learning, and emphasizing high-accuracy detection methods for MCI and seizures.

Contribution

It introduces a novel bibliometric analysis using co-occurrence analysis and the PAGER framework to map EEG research trends in MCI from 2012 to 2022.

Findings

Identification of main research themes in EEG and MCI

High-accuracy detection of MCI and seizures using EEG techniques

Comprehensive mapping of research progress and gaps

Abstract

Background: Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) due to the high rate of progression from MCI to AD. Sensitive neural biomarkers may provide a tool for an accurate MCI diagnosis, enabling earlier and perhaps more effective treatment. Despite the availability of numerous neuroscience techniques, electroencephalography (EEG) is the most popular and frequently used tool among researchers due to its low cost and superior temporal resolution. Objective: We conducted a scoping review of EEG and MCI between 2012 and 2022 to track the progression of research in this field. Methods: In contrast to previous scoping reviews, the data charting was aided by co-occurrence analysis using VOSviewer, while data reporting adopted a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework to increase the quality of the results. Results: Event-related potentials (ERPs) and EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the research themes addressed by 2310 peer-reviewed articles on EEG and MCI. Conclusion: Our review identified the main research themes in EEG and MCI with high-accuracy detection of seizure and MCI performed using ERP/EEG, QEEG and EEG-based machine learning frameworks.