Privacy-Preserving Dementia Classification from EEG via Hybrid-Fusion EEGNetv4 and Federated Learning

Muhammad Umair¹Muhammad Shahbaz Khan²Muhammad Hanif^3*Wad Ghaban⁴Ibtehal Nafea⁵Sultan Noman Mohammed⁶Faisal Saeed⁷

• ¹University of Southern Queensland, Toowoomba, Queensland, Australia
• ²Edinburgh Napier University, Edinburgh, United Kingdom
• ³Örebro University, Örebro, Örebro, Sweden
• ⁴University of Tabuk, Tabuk, Tabuk, Saudi Arabia
• ⁵Taibah University, Medina, Al Madinah, Saudi Arabia
• ⁶Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia
• ⁷Birmingham City University, Birmingham, United Kingdom

As global life expectancy rises, a growing proportion of the population is affected by dementia, particularly Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Electroencephalography (EEG) based diagnosis presents a non-invasive, cost effective alternative for early detection, yet existing methods are challenged by data scarcity, inter-subject variability, and privacy concerns. This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning and Federated Learning (FL). Five convolutional neural networks (EEGNetv1, EEGNetv4, EEGITNet, EEGInception, EEGInceptionERP) have been evaluated on resting-state EEG dataset comprising 88 subjects. EEG signals are preprocessed using band-pass (1-45 Hz) and notch filtering, followed by exponential standardization and 4second windowing. EEGNetv4 outperformed among other EEG tailored models, and upon utilizing the hybrid fusion techniques it achieves 97.1% accuracy using only 1609 parameters and less than 1 MB of memory, demonstrating high efficiency. Moreover, FL using FedAvg is implemented across five stratified clients, achieving 96.9% accuracy on the hybrid fused EEGNetV4 model while preserving data privacy. This work establishes a scalable, resource-efficient, and privacycompliant framework for EEG-based dementia diagnosis, suitable for deployment in real-world clinical and edge-device settings.