用分频图神经专家模型提升阿尔茨海默病的脑电识别准确率

Dementia disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in electroencephalography (EEG) that challenge accurate diagnosis. Existing EEG-based methods are limited by full-band frequency analysis, which hinders precise differentiation of dementia subtypes and severity stages. To address this limitation, we propose a Variational Mixture of Graph Neural Experts (VMoGE) framework that integrates multi-band EEG analysis with variational graph neural networks and a mixture-of-experts architecture. Each expert specializes in a specific EEG frequency band and models brain connectivity using a Gaussian Markov Random Field prior, while a variational gating mechanism adaptively integrates expert outputs. This design enables the model to learn frequency-specific brain network representations while modeling latent uncertainty through variational inference. Experimental results on two EEG dementia datasets show that VMoGE achieves strong performance, with an area under the curve (AUC) of 0.89 for healthy controls (HC) vs. AD classification in the main comparison and competitive results across dementia subtyping and Clinical Dementia Rating (CDR) staging tasks. Clinically, VMoGE offers three key translational values: the expert gating weights correlate with Mini-Mental State Examination (MMSE) scores and CDR severity, slow-wave $δ/ θ$-band contributions are associated with AD-related EEG slowing and disease progression, and spatially localized activation maps reveal posterior $θ$/$α$-band alterations and region-specific $β$-band changes, providing neurophysiologically interpretable patterns aligned with known AD neuropathology.