Virtual reality and machine learning in the automatic photoparoxysmal response detection

Moncada Martins, Fernando; Martín, Sofía; González, V. M.; Álvarez García, Víctor Manuel; García López, B.; Gómez Menéndez, A. I.; Villar Flecha, José Ramón

doi:10.1007/s00521-022-06940-z

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Virtual reality and machine learning in the automatic photoparoxysmal response detection

dc.contributor.author	Moncada Martins, Fernando
dc.contributor.author	Martín, Sofía
dc.contributor.author	González, V. M.
dc.contributor.author	Álvarez García, Víctor Manuel
dc.contributor.author	García López, B.
dc.contributor.author	Gómez Menéndez, A. I.
dc.contributor.author	Villar Flecha, José Ramón
dc.date.accessioned	2022-10-25T11:10:29Z
dc.date.available	2022-10-25T11:10:29Z
dc.date.issued	2023
dc.identifier.citation	Neural Computing and Applications, 35, p. 5643-5659 (2023); doi:10.1007/s00521-022-06940-z
dc.identifier.issn	0941-0643
dc.identifier.uri	http://hdl.handle.net/10651/65145
dc.description.sponsorship	This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R, PID2020-112726RB-I00 and the State Research Agency (AEI, Spain) under grant agreement No RED2018-102312-T (IA-Biomed). Additionally, by the Council of Gijón through the University Institute of Industrial Technology of Asturias grant SV-21-GIJON-1-19.
dc.format.extent	p. 5643-5659
dc.language.iso	eng
dc.relation.ispartof	Neural computing and applications
dc.rights	© 2022 Los autores
dc.rights	CC Reconocimiento 4.0 Internacional
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123836068&doi=10.1007%2fs00521-022-06940-z&partnerID=40&md5=4b1a5a57e8af369a9f11a497a5e46b89
dc.title	Virtual reality and machine learning in the automatic photoparoxysmal response detection
dc.type	journal article
dc.identifier.doi	10.1007/s00521-022-06940-z
dc.local.notes	OA ATUO22
dc.relation.projectID	MINECO-TIN2017-84804-R
dc.relation.projectID	MICINN/PID2020-112726RB-I00
dc.relation.projectID	AEI/RED2018-102312-T
dc.relation.publisherversion	http://dx.doi.org/10.1007/s00521-022-06940-z
dc.rights.accessRights	open access
dc.type.hasVersion	VoR

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