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dc.contributor.authorGöksel Duru, Dilek
dc.contributor.authorAlobaidi, May
dc.date.accessioned2022-01-05T08:14:16Z
dc.date.available2022-01-05T08:14:16Z
dc.date.issued2021en_US
dc.identifier.citationGöksel Duru, D., & Alobaidi, M. (2021). Classification of brain electrophysiological changes in response to colour stimuli. Physical and Engineering Sciences in Medicine, 44(3), 727-743.en_US
dc.identifier.issn2662-4729
dc.identifier.issn2662-4737
dc.identifier.urihttps://hdl.handle.net/20.500.12846/614
dc.description.abstractIn this study, the classification of ongoing brain activity occurring as a response to colour stimuli was managed and reported. Until now, the classification of the seen colour from brain electrical signals has not been investigated or reported in the related literature. In this study, we aimed to classify EEG brain responses corresponding to blue, green, and red coloured shapes. In addition to the current literature, we focused on ongoing EEG responses instead of using ERP metrics, with visual stimulus-related ERP metrics also compared throughout the study. The feature extraction process was carried out using the Fourier transform to obtain the conventional band power values of the EEG for each stimulus type. Delta, theta, alpha, beta, and gamma-band power values of each one-second period constituted the feature set. In addition to scalp measurements, a second feature set was obtained based on the inverse solution of the EEG waves. Furthermore, we applied one-way ANOVA for the feature selection prior to classification procedures. Four classifiers were implemented using the reduced feature set and the raw one as well. The differences between scalp responses were localized mainly around the temporal and temporoparietal regions. Our ERP-component findings support the fact that additional brain regions among the visual cortex participate in the colour categorization process of the brain. RGB colours were identified using 1 s EEG data. Ensemble-KNN and KNN achieved the highest accuracy values (93%) when used either with scalp spectral features or source space features.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s13246-021-01021-2en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectroencephalographyen_US
dc.subjectColour Stimulien_US
dc.subjectClassificationen_US
dc.subjectMachine Learningen_US
dc.subjectEvent Related Potentialsen_US
dc.subjectElektroensefalografien_US
dc.subjectRenk Uyaranlarıen_US
dc.subjectMakine Öğrenmeen_US
dc.subjectElektroenzephalographieen_US
dc.subjectFarbstimulien_US
dc.subjectEinstufungen_US
dc.subjectMaschinelles Lernenen_US
dc.titleClassification of brain electrophysiological changes in response to colour stimulien_US
dc.typearticleen_US
dc.relation.journalPhysical and Engineering Sciences in Medicineen_US
dc.contributor.authorID0000-0003-1484-8603en_US
dc.identifier.volume44en_US
dc.identifier.issue3en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.departmentTAÜ, Fen Fakültesi, Moleküler Biyoteknoloji Bölümüen_US
dc.contributor.institutionauthorGöksel Duru, Dilek
dc.identifier.startpage727en_US
dc.identifier.endpage743en_US


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