Subject-dependent and subject-independent classification of mental arithmetic and silent reading tasks

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dc.contributor.authorArslan, Mustafa Turan
dc.contributor.authorEraldemir, Server Göksel
dc.contributor.authorYıldırım, Esen
dc.date.accessioned2019-07-16T16:01:39Z
dc.date.available2019-07-16T16:01:39Z
dc.date.issued2017
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractIn this study, the electrical activities in the brain were classified during mental mathematical tasks and silent text reading. EEG recordings are collected from 18 healthy male university/college students, ages ranging from 18 to 25. During the study, a total of 60 slides including verbal text reading and arithmetical operations were presented to the subjects. EEG signals were collected from 26 channels in the course of slide show. Features were extracted by employing Hilbert Huang Transform (HHT). Then, subjectdependent and subject-independent classifications were performed using k-Nearest Neighbor (k-NN) algorithm with parameters k=1, 3, 5 and 10. Subject-dependent classifications resulted in accuracy rates between 95.8% and 99%, whereas the accuracy rates were between 92.2% and 97% for subject independent classification. The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysisen_US
dc.description.abstractIn this study, the electrical activities in the brain were classified during mental mathematical tasks and silent text reading. EEG recordings are collected from 18 healthy male university/college students, ages ranging from 18 to 25. During the study, a total of 60 slides including verbal text reading and arithmetical operations were presented to the subjects. EEG signals were collected from 26 channels in the course of slide show. Features were extracted by employing Hilbert Huang Transform (HHT). Then, subjectdependent and subject-independent classifications were performed using k-Nearest Neighbor (k-NN) algorithm with parameters k=1, 3, 5 and 10. Subject-dependent classifications resulted in accuracy rates between 95.8% and 99%, whereas the accuracy rates were between 92.2% and 97% for subject independent classification. The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysisen_US
dc.identifier.endpage195en_US
dc.identifier.issn1308-5514
dc.identifier.issue3en_US
dc.identifier.startpage186en_US
dc.identifier.urihttps://trdizin.gov.tr/publication/paper/detail/TWpneU1EYzRPQT09
dc.identifier.urihttps://hdl.handle.net/20.500.12483/2778
dc.identifier.volume9en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofUluslararası Mühendislik Araştırma ve Geliştirme Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMühendisliken_US
dc.subjectOrtak Disiplinleren_US
dc.titleSubject-dependent and subject-independent classification of mental arithmetic and silent reading tasksen_US
dc.typeArticleen_US

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