Abstract
Alzheimer’s disease (AD) is a leading cause of dementia, yet its diagnosis remains challenging. EEG provides a noninvasive and cost-effective method for monitoring brain activity, which may reflect both cognitive decline and altered emotional states. In this study, an EEG-based pipeline was developed to classify AD using two approaches: an ensemble of boosting classifiers based on extracted features, and a deep convolutional neural network (CNN) applied to raw signals. A publicly available dataset was processed to extract time, frequency, and complexity features, with emotional brain dynamics implicitly reflected in the signals and considered during analysis. Five ensemble models (including CatBoost, LightGBM, and XGBoost) were optimized using Bayesian search. The CNN was trained separately and evaluated under cross-validation schemes. A balanced accuracy of 78.96% was achieved for AD detection using XGBoost, while the CNN reached 70.92% for Frontotemporal dementia. The study demonstrates that combining machine learning with EEG produces generalizable models for dementia detection and suggests that accounting for emotion-related variability may enhance diagnostic results.