Machine Learning and Deep Learning for EEG-Based Dementia Prediction: A Comprehensive Survey

Abstract

Dementia-related disorders, particularly Alzheimer’s disease (AD), represent a growing global health challenge, increasing the need for early and reliable detection. Electroencephalography (EEG), a non-invasive and cost-effective neurophysiological modality, has emerged as a promising tool for identifying neural signatures associated with cognitive decline. Recent advances in machine learning (ML) and deep learning (DL) have enabled more effective analysis of complex EEG signals for automated dementia prediction. This survey provides a comprehensive synthesis of EEG-based dementia studies published between 2020 and 2025, with a primary focus on Alzheimer’s disease (AD), frontotemporal dementia (FTD), mild cognitive impairment (MCI), and related dementia disorders. Unlike previous reviews that emphasize multimodal approaches or specific methodologies, this work exclusively focuses on EEG and presents a systematic comparison of ML and DL approaches, preprocessing pipelines, feature extraction techniques, publicly available datasets, validation strategies, performance metrics, and explainable methods. We further examine current research trends, identify methodological limitations such as small dataset sizes, subject-level data leakage, class imbalance, inconsistent preprocessing protocols, and limited multicenter validation, and discuss their implications for model generalizability and clinical adoption. We conclude by outlining future research directions toward developing robust, interpretable, and scalable EEG-based dementia prediction systems suitable for real-world clinical applications.