DMAE-EEG: A Pretraining Framework for EEG Spatiotemporal Representation Learning

Electroencephalography (EEG) plays a crucial role in neuroscience research and clinical practice, but it remains limited by nonuniform data, noise, and difficulty in labeling. To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining ge...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 36; no. 10; pp. 17664 - 17678
Main Authors:	Zhang, Yifan, Yu, Yang, Li, Hao, Wu, Anqi, Chen, Xin, Liu, Jinfang, Zeng, Ling-Li, Hu, Dewen
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.10.2025
Subjects:	Algorithms Autoencoders Brain - physiology Brain modeling Databases, Factual Decoding Electrodes Electroencephalography Electroencephalography (EEG) Electroencephalography - methods Feature extraction Humans Machine Learning masked autoencoder motion intention recognition Motors Neural Networks, Computer Noise Representation learning Signal Processing, Computer-Assisted signal quality enhancement Signal-To-Noise Ratio Spatio-Temporal Analysis Spatiotemporal phenomena
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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