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

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	AIMONE, Christopher Allen, WOOD, Sean Ulrich Niethe, JACOB BANVILLE, Hubert
Format:	Patent
Sprache:	eng ; fre ; ger
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
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue
container_start_page
container_title
container_volume
creator	AIMONE, Christopher Allen WOOD, Sean Ulrich Niethe JACOB BANVILLE, Hubert
description	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 detection. Experiments are performed on two large public datasets with thousands of recordings and perform baseline comparisons with purely supervised and hand-engineered paradigms.
format	Patent
fullrecord	<record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_EP4189607A4</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>EP4189607A4</sourcerecordid><originalsourceid>FETCH-epo_espacenet_EP4189607A43</originalsourceid><addsrcrecordid>eNrjZHD1cXUM8vP0c1cIcg0Icg129QtxDPH09wtW8HdTcHV1Vwj2dPdz9AlWCPcM8VAIdvVx0w0ODXANCvMMdnVRgGnmYWBNS8wpTuWF0twMCm6uIc4euqkF-fGpxQWJyal5qSXxrgEmhhaWZgbmjibGRCgBAE_-K_o</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>LEARNING REPRESENTATIONS OF EEG SIGNALS WITH SELF-SUPERVISED LEARNING</title><source>esp@cenet</source><creator>AIMONE, Christopher Allen ; WOOD, Sean Ulrich Niethe ; JACOB BANVILLE, Hubert</creator><creatorcontrib>AIMONE, Christopher Allen ; WOOD, Sean Ulrich Niethe ; JACOB BANVILLE, Hubert</creatorcontrib><description>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 detection. Experiments are performed on two large public datasets with thousands of recordings and perform baseline comparisons with purely supervised and hand-engineered paradigms.</description><language>eng ; fre ; ger</language><subject>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</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240731&DB=EPODOC&CC=EP&NR=4189607A4$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240731&DB=EPODOC&CC=EP&NR=4189607A4$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>AIMONE, Christopher Allen</creatorcontrib><creatorcontrib>WOOD, Sean Ulrich Niethe</creatorcontrib><creatorcontrib>JACOB BANVILLE, Hubert</creatorcontrib><title>LEARNING REPRESENTATIONS OF EEG SIGNALS WITH SELF-SUPERVISED LEARNING</title><description>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 detection. Experiments are performed on two large public datasets with thousands of recordings and perform baseline comparisons with purely supervised and hand-engineered paradigms.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DIAGNOSIS</subject><subject>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</subject><subject>HUMAN NECESSITIES</subject><subject>HYGIENE</subject><subject>IDENTIFICATION</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>MEDICAL OR VETERINARY SCIENCE</subject><subject>PHYSICS</subject><subject>SURGERY</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHD1cXUM8vP0c1cIcg0Icg129QtxDPH09wtW8HdTcHV1Vwj2dPdz9AlWCPcM8VAIdvVx0w0ODXANCvMMdnVRgGnmYWBNS8wpTuWF0twMCm6uIc4euqkF-fGpxQWJyal5qSXxrgEmhhaWZgbmjibGRCgBAE_-K_o</recordid><startdate>20240731</startdate><enddate>20240731</enddate><creator>AIMONE, Christopher Allen</creator><creator>WOOD, Sean Ulrich Niethe</creator><creator>JACOB BANVILLE, Hubert</creator><scope>EVB</scope></search><sort><creationdate>20240731</creationdate><title>LEARNING REPRESENTATIONS OF EEG SIGNALS WITH SELF-SUPERVISED LEARNING</title><author>AIMONE, Christopher Allen ; WOOD, Sean Ulrich Niethe ; JACOB BANVILLE, Hubert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP4189607A43</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DIAGNOSIS</topic><topic>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</topic><topic>HUMAN NECESSITIES</topic><topic>HYGIENE</topic><topic>IDENTIFICATION</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>MEDICAL OR VETERINARY SCIENCE</topic><topic>PHYSICS</topic><topic>SURGERY</topic><toplevel>online_resources</toplevel><creatorcontrib>AIMONE, Christopher Allen</creatorcontrib><creatorcontrib>WOOD, Sean Ulrich Niethe</creatorcontrib><creatorcontrib>JACOB BANVILLE, Hubert</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>AIMONE, Christopher Allen</au><au>WOOD, Sean Ulrich Niethe</au><au>JACOB BANVILLE, Hubert</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>LEARNING REPRESENTATIONS OF EEG SIGNALS WITH SELF-SUPERVISED LEARNING</title><date>2024-07-31</date><risdate>2024</risdate><abstract>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 detection. Experiments are performed on two large public datasets with thousands of recordings and perform baseline comparisons with purely supervised and hand-engineered paradigms.</abstract><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier
ispartof
issn
language	eng ; fre ; ger
recordid	cdi_epo_espacenet_EP4189607A4
source	esp@cenet
subjects	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
title	LEARNING REPRESENTATIONS OF EEG SIGNALS WITH SELF-SUPERVISED LEARNING
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T18%3A49%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=AIMONE,%20Christopher%20Allen&rft.date=2024-07-31&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EEP4189607A4%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true