Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning

Umair, Muhammad and Khan, Muhammad Shahbaz and Hanif, Muhammad and Ghaban, Wad and Nafea, Ibtehal and Qasem, Sultan Noman and Saeed, Faisal (2025) Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning. Frontiers in Computational Neuroscience, 19. ISSN 1662-5188

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Abstract

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 4-second windowing. EEGNetv4 outperformed among other EEG tailored models, and upon utilizing the hybrid fusion techniques it achieves 97.1% accuracy using only 1,609 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 privacy-compliant framework for EEG-based dementia diagnosis, suitable for deployment in real-world clinical and edge-device settings.

Item Type:	Article
Identification Number:	10.3389/fncom.2025.1617883
Dates:	Date Event 24 July 2025 Accepted 18 August 2025 Published Online
Uncontrolled Keywords:	neurobehavior analysis, EEGNET, dementia, federated learning, deep learning, smart healthcare, hybrid-fusion, FedAvg
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	09 Mar 2026 13:05
Last Modified:	09 Mar 2026 13:06
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16914

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