Background: As the prevalence of Alzheimer's disease (AD) grows with an aging population, the need for early diagnosis has led to increased focus on electroencephalography (EEG) as a non-invasive diagnostic tool.
Objective: This review assesses advancements in EEG analysis, including the application of machine learning, for detecting AD from 2000 to 2023.
Methods: Following PRISMA guidelines, a search across major databases resulted in 25 studies that met the inclusion criteria, focusing on EEG's application in AD diagnosis and the use of novel signal processing and machine learning techniques.