Investigation of feature selection algorithms on a cognitive task classification : a comparison study

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dc.contributor.authorEraldemir, Servet Göksel
dc.contributor.authorArslan, Mustafa Turan
dc.contributor.authorYıldırım, Esen
dc.date.accessioned2019-07-16T16:02:07Z
dc.date.available2019-07-16T16:02:07Z
dc.date.issued2018
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractIn this study, the effects of feature selection on classification of the electrical signals generated in the brain during numerical and verbal operations are investigated. 18 healthy university/college students were chosen for the experimental study. EEG signals were recorded during silent reading and mental arithmetic operations without using any pen and paper. A total of 60 slides, 30 of which contained reading passages and the rest contained arithmetic operations, were presented in the experiment. EEG signals recorded from 26 channels during the slide show. The recorded EEG signals were analyzed by Hilbert Huang Transform (HHT), and then features were extracted. 312 features were classified by Bayesian Network algorithm without applying feature selection with 92.60% average accuracy. Consistency measures and Correlation based Feature Selection methods were, then, used for feature selection and the numbers of selected features are 8 and 39 on average, respectively. Classification accuracies by using these feature selection algorithms were obtained as 93.98% and 95.58%, respectively. The results showed that feature selection algorithms contribute positively to the classification performance.en_US
dc.identifier.endpage104en_US
dc.identifier.issue2en_US
dc.identifier.startpage99en_US
dc.identifier.urihttps://trdizin.gov.tr/publication/paper/detail/TWpreU1EYzBOQT09
dc.identifier.urihttps://hdl.handle.net/20.500.12483/2891
dc.identifier.volume6en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofBalkan Journal of Electrical and Computer Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYapay Zekaen_US
dc.subjectUzaktan Algılamaen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectSibernitiken_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectTeori ve Metotlaren_US
dc.subjectMühendisliken_US
dc.subjectElektrik ve Elektroniken_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectDonanım ve Mimarien_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectBilgi Sistemlerien_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectDisiplinler Arası Uygulamalaren_US
dc.subjectMühendislİKen_US
dc.subjectBiyotıpen_US
dc.subjectYeşilen_US
dc.subjectSürdürülebilir Bilim ve Teknolojien_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYazılım Mühendisliğien_US
dc.subjectTelekomünikasyonen_US
dc.titleInvestigation of feature selection algorithms on a cognitive task classification : a comparison studyen_US
dc.typeArticleen_US

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