Principal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentials

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dc.authorid0000-0002-3087-541X
dc.authorid0000-0002-2150-4756
dc.contributor.authorYesilkaya, Bartu
dc.contributor.authorSayilgan, Ebru
dc.contributor.authorYuce, Yilmaz Kemal
dc.contributor.authorPerc, Matjaz
dc.contributor.authorIsler, Yalcin
dc.date.accessioned2026-01-24T12:31:15Z
dc.date.available2026-01-24T12:31:15Z
dc.date.issued2023
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractSteady-state visually evoked potentials (SSVEP) are stochastic and nonstationary bioelectric signals. Because of these properties, it is difficult to achieve high classification accuracy, especially when many considered features lead to a complex structure. We therefore propose a manifold learning framework to decrease the number of features and to classify SSVEP data by comparing lower dimensional matrices with well-known machine learning algorithms. We use the AVI-SSVEP Dataset, which includes stimuli at seven different frequencies and 15360 samples per person. The SSVEP features are extracted from relevant and distinctive frequency -domain, time-domain, and time-frequency domain properties, creating a total of 55 feature vectors. We then analyze and compare five divergent manifold learning methods with respect to their performance on nine different machine-learning algorithms. Among all considered manifold learning methods, we show that the Principal Component Analysis has the best classifier performance with an average of 22 components. Moreover, the Naive Bayes classifier with the Principal Component Analysis achieves the maximum accuracy of 50.0%-80.95% for a 7-class classification problem. Our research thus shows that the proposed analytical framework can significantly improve the decoding accuracy of 7-class SSVEP problems, and that it exhibits notable robustness and efficiency for small group datasets.
dc.description.sponsorshipSlovenian Research Agency (Javna agencija za raziskovalno dejavnost Republike Slovenije) [P1-0403, J1-2457]
dc.description.sponsorshipMatja? Perc was supported by the Slovenian Research Agency (Javna agencija za raziskovalno dejavnost Republike Slovenije) (Grant Nos. P1-0403 and J1-2457) .
dc.identifier.doi10.1016/j.jocs.2023.102000
dc.identifier.issn1877-7503
dc.identifier.issn1877-7511
dc.identifier.scopus2-s2.0-85151475497
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jocs.2023.102000
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5744
dc.identifier.volume68
dc.identifier.wosWOS:000965027200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Computational Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectManifold learning
dc.subjectBrain-computer interface
dc.subjectSteady-state visual evoked potential
dc.subjectPrincipal component analysis
dc.subjectFeature reduction
dc.titlePrincipal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentials
dc.typeArticle

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