ADAST: Attentive cross-domain EEG-based sleep staging framework with iterative self-training

• Main Authors: Eldele, Emadeldeen, Ragab, Mohamed, Chen, Zhenghua, Wu, Min, Kwoh, Chee Keong, Li, Xiaoli, Guan, Cuntai
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Unsupervised Domain Adaptation; Adversarial Training
• Online Access: https://hdl.handle.net/10356/164490
• Institution: Nanyang Technological University

Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a large public labeled sleep dataset and test it on a smaller one with subjects o...