LEARNING REPRESENTATIONS OF EEG SIGNALS WITH SELF-SUPERVISED LEARNING

Self-supervised learning (SSL) is used to leverage structure in unlabeled data, to learn representations of EEG signals. Two tasks based on temporal context prediction as well as contrastive predictive coding are applied to two clinically-relevant problems: EEG-based sleep staging and pathology dete...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	AIMONE, CHRISTOPHER ALLEN, JACOB BANVILLE, HUBERT, WOOD, SEAN ULRICH NIETHE
Format:	Patent
Sprache:	eng ; fre
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DIAGNOSIS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA HUMAN NECESSITIES HYGIENE IDENTIFICATION INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY
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