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Enhanced use practices in SSVEP-based BCIs using an analytical approach of canonical correlation analysis

dc.contributor.authorFerres Brogin, Joao Angelo [UNESP]
dc.contributor.authorFaber, Jean
dc.contributor.authorBueno, Douglas Domingues [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.date.accessioned2020-12-10T19:44:08Z
dc.date.available2020-12-10T19:44:08Z
dc.date.issued2020-01-01
dc.description.abstractThe search for a better understanding of the brain's anatomy and its functions on human actions has been a harsh yet very useful task, especially for brain-computer interface (BCI) engineering applications and medical diagnosis using signals from patients. Analyses involving electroencephalogram (EEG) signals processing have proven to be of great significance for developing this field of study. A widely used approach for this purpose is a BCI based on steady-state visual-evoked potentials (SSVEP), which, in general, are signals characterized by the brain's evoked response to visual stimuli modulated at a certain frequency. This work aims thus to propose a generalization of the correlation coefficient, which entails canonical correlation analysis (CCA), and verify its behavior under varying parameters to establish better use practices in BCI applications, comprising physiological, technical and operational factors. Also, it aims to analyze and compare signals from an SSVEP-based BCI to the results obtained from this generalization. The results show that new parameters can be introduced to better select the stimulus frequency and choose a specific BCI application; also, the analytical equation presents a good match with results obtained from real signals; at last, the final CCA equation can be written as a more general rule based on the sampling rate ratio, thus ensuring a higher flexibility and reliability for this technique. (C) 2019 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUniv Estadual Paulista, Dept Mech Engn, Ave Brasil 56, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.affiliationUniv Fed Sao Paulo, Dept Neurol & Neurosurg, Rua Pedro de Toledo 669, Sao Paulo, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, Dept Math, Alameda Rio Janeiro 266, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Mech Engn, Ave Brasil 56, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Math, Alameda Rio Janeiro 266, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdCNPq: 130973/2017-3
dc.format.extent13
dc.identifierhttp://dx.doi.org/10.1016/j.bspc.2019.101644
dc.identifier.citationBiomedical Signal Processing And Control. Oxford: Elsevier Sci Ltd, v. 55, 13 p., 2020.
dc.identifier.doi10.1016/j.bspc.2019.101644
dc.identifier.issn1746-8094
dc.identifier.urihttp://hdl.handle.net/11449/196414
dc.identifier.wosWOS:000502893200031
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofBiomedical Signal Processing And Control
dc.sourceWeb of Science
dc.subjectBrain-computer interface
dc.subjectSteady-state visual-evoked potentials
dc.subjectCanonical correlation analysis
dc.titleEnhanced use practices in SSVEP-based BCIs using an analytical approach of canonical correlation analysisen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.departmentEngenharia Mecânica - FEISpt
unesp.departmentMatemática - FEISpt

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