This paper is in the following e-collection/theme issue:

Foundations of AI (47) Artificial Intelligence (1421)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46769, first published .
Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Authors of this article:

Chaoqi Yang1 Author Orcid Image ;   Cao Xiao2 Author Orcid Image ;   M Brandon Westover3 Author Orcid Image ;   Jimeng Sun1 Author Orcid Image
Article Authors Cited by (6) Tweetations (2) Metrics

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  1. Wu H, Li S, Wu D. Motor Imagery Classification for Asynchronous EEG-Based Brain–Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:527 View
  2. Wang R, Chen Z. Large-scale foundation models and generative AI for BigData neuroscience. Neuroscience Research 2024 View
  3. Wang I, Lee C, Kim H, Kim D. Negative-Sample-Free Contrastive Self-Supervised Learning for Electroencephalogram-Based Motor Imagery Classification. IEEE Access 2024;12:132714 View
  4. Wang P, Zheng H, Dai S, Wang Y, Gu X, Wu Y, Wang X. A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis. Chinese Journal of Information Fusion 2024;1(3):183 View