EEG-Based Detection of Alzheimer's Disease: A Bibliometric Analysis

Ehssan AL Janabi

doi:10.58564/IJSER.5.3.2026.375

Authors

Ehssan AL Janabi Dept. of Computer Engineering, Karabuk University, Karabuk, Turkey

DOI:

https://doi.org/10.58564/IJSER.5.3.2026.375

Keywords:

Alzheimer's disease detection (ADD), bibliometric analysis, EEG, Deep Learning.

Abstract

Alzheimer’s disease (AD) is a progressive brain disorder commonly seen in older adults. It leads to memory loss, cognitive decline, and eventually affects a person's ability to perform everyday tasks. Over time, it can become life-threatening, and its prevalence has been rising significantly. One widely used approach for detecting AD is through electroencephalography (EEG), which measures electrical activity in the brain. This paper contains an overview of detecting Alzheimer’s disease (AD) with EEG in a bibliometric analysis on the publication statistics provided from the Web of Science (WoS) database. This analysis includes basic statistical inferences on the publication counts, yearly distributions, citation performances, distribution on indexing, publication title, countries, etc., in a comparative manner to outline how EEG-based diagnosis differs from the general AD detection studies.

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