Self-supervised representation learning from electroencephalography signals

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dc.contributor.author Banville, Hubert
dc.contributor.author Albuquerque, Isabela
dc.contributor.author Hyvärinen, Aapo
dc.contributor.author Moffat, Grame
dc.contributor.author Engemann, Denis-Alexander
dc.contributor.author Gramfort, Alexandre
dc.date.accessioned 2020-07-02T11:43:02Z
dc.date.available 2020-07-02T11:43:02Z
dc.date.issued 2019
dc.identifier.citation Banville , H , Albuquerque , I , Hyvärinen , A , Moffat , G , Engemann , D-A & Gramfort , A 2019 , Self-supervised representation learning from electroencephalography signals . in 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) . vol. 2019 , IEEE , IEEE International Workshop on Machine Learning for Signal Processing , Pittsburgh , United States , 13/10/2019 . https://doi.org/10.1109/MLSP.2019.8918693
dc.identifier.citation conference
dc.identifier.other PURE: 128939713
dc.identifier.other PURE UUID: 31b91f7e-c7a9-4c6b-a56b-2db28582fde4
dc.identifier.other ORCID: /0000-0002-5806-4432/work/66564106
dc.identifier.other Scopus: 85077707750
dc.identifier.other WOS: 000534480500006
dc.identifier.uri http://hdl.handle.net/10138/317290
dc.format.extent 6
dc.language.iso eng
dc.publisher IEEE
dc.relation.ispartof 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
dc.relation.isversionof 978-1-7281-0824-7
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.subject Self-supervised learning
dc.subject representation learning
dc.subject electroencephalography
dc.subject time series
dc.title Self-supervised representation learning from electroencephalography signals en
dc.type Conference contribution
dc.contributor.organization Helsinki Institute for Information Technology
dc.contributor.organization Department of Computer Science
dc.contributor.organization Neuroinformatics research group / Aapo Hyvärinen
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1109/MLSP.2019.8918693
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion
dc.identifier.url https://ieeexplore.ieee.org/xpl/conhome/8911118/proceeding

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