Efficient identification of Alzheimer’s brain dynamics with Spatial-Temporal Autoencoder: A deep learning approach for diagnosing brain disorders

•An automatic EEG-based diagnosis scheme for Alzheimer’s disease (AD) with deep learning methods was proposed.•A Spatial-Temporal Autoencoder (STAE) framework was designed to estimate latent factors of EEG via unsupervised learning.•Alzheimer’s brain dynamics were inferred with STAE and manifolds in...

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Bibliographic Details

Published in:	Biomedical signal processing and control Vol. 86; p. 104917
Main Authors:	Wu, Lingyun, Zhao, Quanfa, Liu, Jing, Yu, Haitao
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2023
Subjects:	Alzheimer’s disease Deep learning Diagnosis Latent brain dynamics Spatial-Temporal Autoencoder Deep learning Latent brain dynamics Diagnosis Alzheimer’s disease Spatial-Temporal Autoencoder
ISSN:	1746-8094, 1746-8108
Online Access:	Get full text
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