Neurodegenerative Disease Classification with EEG: A Deep Learning Approach for Dementia Diagnosis

Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) are two of the most common neurodegenerative disorders, most commonly leading to progressive cognitive decline. Electroencephalography (EEG) has been proven as a promising tool for early and non-invasive detection of these disorders, leveraging advanced computational methods for classification. In this study, we propose a novel Convolutional Neural Network (CNN) architecture for the automatic classification of AD and FTD using EEG-based biomarkers. Our approach utilizes Power Spectral Density derived Relative Band Power and Spectral Coherence Index as input features, extracted from preprocessed EEG signals of 88 participants following Independent Component Analysis and Artifact Subspace Reconstruction for noise reduction. The extracted features capture both spectral power variations and functional connectivity disruptions, key findings of neurodegenerative disorders. The proposed CNN model is trained and validated using a Leave-One-Subject-Out crossvalidation approach to ensure robust generalization. Experimental results demonstrate that our model achieves high classification accuracy, outperforming state-of-the-art machine learning methods and achieving competitive performance with state-of-the-art deep learning approaches. These findings highlight the effectiveness of EEG-based deep learning models for differential dementia diagnosis, providing a potential avenue for early detection and clinical decision support.