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

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

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 exi...

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Bibliographic Details
Main Authors: Muhammad Umair, Muhammad Shahbaz Khan, Muhammad Hanif, Wad Ghaban, Ibtehal Nafea, Sultan Noman Qasem, Faisal Saeed
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Computational Neuroscience
Subjects:
neurobehavior analysis
EEGNET
dementia
federated learning
deep learning
smart healthcare
Online Access:https://www.frontiersin.org/articles/10.3389/fncom.2025.1617883/full
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Summary: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.
ISSN:1662-5188

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