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dc.contributor.author | Martínez Cagigal, Víctor | |
dc.contributor.author | SantaMaría Vazquez, Eduardo | |
dc.contributor.author | Pérez Velasco, Sergio | |
dc.contributor.author | Marcos Martínez, Diego | |
dc.contributor.author | Moreno Calderón, Selene | |
dc.contributor.author | Hornero Sánchez, Roberto | |
dc.date.accessioned | 2023-06-26T10:58:46Z | |
dc.date.available | 2023-06-26T10:58:46Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Expert Systems with Applications, 2023, vol. 232, 120815 | es |
dc.identifier.issn | 0957-4174 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/59955 | |
dc.description | Producción Científica | es |
dc.description.abstract | Code-modulated visual evoked potentials (c-VEPs) have marked a milestone in the scientific literature due to their ability to achieve reliable, high-speed brain–computer interfaces (BCIs) for communication and control. Generally, these expert systems rely on encoding each command with shifted versions of binary pseudorandom sequences, i.e., flashing black and white targets according to the shifted code. Despite the excellent results in terms of accuracy and selection time, these high-contrast stimuli cause eyestrain for some users. In this work, we propose the use of non-binary p-ary m-sequences, whose levels are encoded with different shades of gray, as a more pleasant alternative than traditional binary codes. The performance and visual fatigue of these p-ary m-sequences, as well as their ability to provide reliable c-VEP-based BCIs, are analyzed for the first time. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Biomedical engineering | es |
dc.subject | Neurosciences | es |
dc.subject.classification | Non-binary codes | es |
dc.subject.classification | Brain–computer interface (BCI) | es |
dc.subject.classification | Electroencephalography (EEG) | es |
dc.subject.classification | Códigos no binarios | es |
dc.subject.classification | Interfaz cerebro-computadora (BCI) | es |
dc.subject.classification | Electroencefalografía (EEG) | es |
dc.title | Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2023 The Authors | es |
dc.identifier.doi | 10.1016/j.eswa.2023.120815 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417423013179?via%3Dihub | es |
dc.identifier.publicationfirstpage | 120815 | es |
dc.identifier.publicationtitle | Expert Systems with Applications | es |
dc.identifier.publicationvolume | 232 | es |
dc.peerreviewed | SI | es |
dc.description.project | Ministerio de Ciencia e Innovación/AEI- FEDER [TED2021-129915B-I00, RTC2019-007350-1 y PID2020-115468RB-I00] | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 32 Ciencias Médicas | es |
dc.subject.unesco | 2490 Neurociencias | es |
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