A Two-Stage EEG Microstate Fusion Framework for Dementia Screening and Alzheimer's Disease/Frontotemporal Dementia Differentiation.

Overview

1. Fudan Institute on Ageing, Fudan University, Shanghai 200433, China
2. Laboratory of Intelligent Home Appliances, College of Science and Technology, Ningbo University, Ningbo 315300, China; (Y.C.); (Y.H.); (J.L.); (X.Z.)
3. MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai 200433, China

Abstract

Differentiating Alzheimer’s disease (AD) from frontotemporal dementia (FTD) using resting-state electroencephalography (EEG) remains clinically challenging because of their overlapping electrophysiological characteristics. Although EEG suits large-scale dementia screening, current method often overestimates performance because of epoch-level data leakage and multiclass feature competition in unified models. We propose a task-decoupled, two-stage hierarchical deep learning framework utilizing multiband EEG microstate dynamics. Continuous microstate sequences, modeled via Hungarian matching to preserve fine-grained temporal information, are processed using a normalizer-free 1D convolutional neural network (1D-CNN-NFNet) integrated with multi-head attention. By decoupling the workflow, Stage 1 performs generalized dementia screening using alpha and delta microstates, achieving an area under the curve (AUC) of 0.851. Stage 2 disentangles AD from FTD using delta and theta dynamics, yielding an AD-locking specificity of 86.1%. Evaluated under a strict subject-level leave-one-subject-out (LOSO) cross-validation protocol, the two-stage framework achieved 63.9% balanced accuracy, outperforming the single-stage baseline (55.4%) with a negligible inference latency of 0.733 ms. Furthermore, attention-based interpretability analysis links frequency-specific microstate alterations to underlying cortical disconnection syndromes. These results demonstrate that the framework provides a reproducible and interpretable auxiliary reference for dementia screening and subtyping in clinical neurology.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data Availability Statement

The public clinical EEG dataset used in this study is available at the OpenNeuro repository (dataset ds004504). The original contributions, data, and code presented in this study are openly available on GitHub at: https://github.com/winnile520-sys/AD-FTD-HC-data-and-code (accessed on 30 March 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 5 MeSH terms, 3 funders, 44 references.

Cite

This paper

Jiang, L., Chen, Y., He, Y., Liang, J., Zhao, X., & Guo, X. (2026). A Two-Stage EEG Microstate Fusion Framework for Dementia Screening and Alzheimer's Disease/Frontotemporal Dementia Differentiation. Biosensors, 16(5), 258. https://doi.org/10.3390/bios16050258

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