TY  -  
T1  - Uncovering the structure of clinical EEG signals with self-supervised learning 
SN  -  /  
UR  - http://hdl.handle.net/10138/342193 
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A1  - Banville, Hubert; Chehab, Omar; Hyvarinen, Aapo; Engemann, Denis-Alexander; Gramfort, Alexandre 
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PB  -  
Y1  - 2021 
LA  - eng 
AB  - Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approache... 
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KW  - self-supervised learning; representation learning; machine learning; electroencephalography; sleep staging; pathology detection; clinical neuroscience; 3112 Neurosciences 
N1  -   
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ER  -