Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces

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dc.contributor.authorSayılgan, Ebru
dc.contributor.authorYüce, Yılmaz Kemal
dc.contributor.authorİşler, Yalçın
dc.date.accessioned2022-09-22T06:10:44Z
dc.date.available2022-09-22T06:10:44Z
dc.date.issued2021
dc.departmentALKÜ, Fakülteler, Rafet Kayış Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractWavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals; however, there exists no evaluation for steady-state visually-evoked potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore, if any, the mother wavelet that suits best to represent SSVEP signals for classification purposes in BCIs. In this study, three common wavelet-based features (variance, energy, and entropy) extracted from SSVEP signals for five distinct EEG frequency bands (delta, theta, alpha, beta, and gamma) were classified to determine three different user commands using six fundamental classifier algorithms. The study was repeated for six different commonly-used mother wavelet functions (haar, daubechies, symlet, coiflet, biorthogonal, and reverse biorthogonal). The best discrimination was obtained with an accuracy of 100% and the average of 75.85%. Besides, ensemble learner gives the highest accuracies for half of the trials. Haar wavelet had the best performance in representing SSVEP signals among other all mother wavelets adopted in this study. Concomitantly, all three features of energy, variance, and entropy should be used together since none of these features had superior classifier performance alone.
dc.identifier.doi10.3906/elk-2010-26
dc.identifier.endpage2279en_US
dc.identifier.issue5en_US
dc.identifier.startpage2263en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12868/1641
dc.identifier.urihttps://journals.tubitak.gov.tr/elektrik/vol29/iss5/1/
dc.identifier.volume29en_US
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectSteady-state visually-evoked potentialsbrain-computer interfaceswavelet transformmother wavelet selectionpattern recognition
dc.subjectSteady-state visually-evoked potentials
dc.subjectBrain-computer interfaces
dc.subjectWavelet transform
dc.subjectMother wavelet selection
dc.subjectPattern recognition
dc.titleEvaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces
dc.typeArticle

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