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dc.contributor.authorMoncada, Fernando
dc.contributor.authorMartín, Sofía
dc.contributor.authorGonzález, Víctor M.
dc.contributor.authorÁlvarez, Víctor M.
dc.contributor.authorGarcía López, Beatriz 
dc.contributor.authorGómez Menéndez, Ana Isabel
dc.contributor.authorVillar, José R.
dc.date.accessioned2024-02-02T15:56:02Z
dc.date.available2024-02-02T15:56:02Z
dc.date.issued2022
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/10259/8567
dc.description.abstractPhotosensitivity, in relation to epilepsy, is a genetically determined condition in which patients have epileptic seizures of different severity provoked by visual stimuli. It can be diagnosed by detecting epileptiform discharges in their electroencephalogram (EEG), known as photoparoxysmal responses (PPR). The most accepted PPR detection method—a manual method—considered as the standard one, consists in submitting the subject to intermittent photic stimulation (IPS), i.e. a flashing light stimulation at increasing and decreasing flickering frequencies in a hospital room under controlled ambient conditions, while at the same time recording her/his brain response by means of EEG signals. This research focuses on introducing virtual reality (VR) in this context, adding, to the conventional infrastructure a more flexible one that can be programmed and that will allow developing a much wider and richer set of experiments in order to detect neurological illnesses, and to study subjects’ behaviours automatically. The loop includes the subject, the VR device, the EEG infrastructure and a computer to analyse and monitor the EEG signal and, in some cases, provide feedback to the VR. As will be shown, AI modelling will be needed in the automatic detection of PPR, but it would also be used in extending the functionality of this system with more advanced features. This system is currently in study with subjects at Burgos University Hospital, Spain.en
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofNeural Computing and Applications. 2022, V. 35, n. 8, p. 5643-5659en
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectElectroencefalogramen
dc.subjectVirtual realityen
dc.subjectPhotoparoxysmal responseen
dc.subjectMachine learningen
dc.subject.otherNeurologíaes
dc.subject.otherNeurologyen
dc.subject.otherFisiologíaes
dc.subject.otherPhysiologyen
dc.subject.otherSaludes
dc.subject.otherHealthen
dc.titleVirtual reality and machine learning in the automatic photoparoxysmal response detectionen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1007/S00521-022-06940-Zes
dc.identifier.doi10.1007/S00521-022-06940-Z
dc.identifier.essn1433-3058
dc.journal.titleNeural Computing and Applicationsen
dc.volume.number35es
dc.issue.number8es
dc.page.initial5643es
dc.page.final5659es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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