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dc.contributor.authorOzel, Pinar
dc.contributor.authorAkan, Aydin
dc.contributor.authorYilmaz, Bulent
dc.date.accessioned2021-05-20T11:00:04Z
dc.date.available2021-05-20T11:00:04Z
dc.date.issued2018en_US
dc.identifier.isbn978-1-5386-6852-8
dc.identifier.urihttps://hdl.handle.net/20.500.12573/732
dc.description.abstractEmotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post -processing technique to compose a localized time -frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self assessment -mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM.en_US
dc.description.sponsorshipBiyomedikal Klinik Muhendisligi Dernegi; Izmir Katip Celebi Univ, Biyomedikal Muhendisligi Bolumuen_US
dc.language.isoengen_US
dc.publisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USAen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMultivariate Syncyrosqueezing Transformen_US
dc.subjectEmotion Recognitionen_US
dc.subjectEEGen_US
dc.titleEmotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Modelen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.relation.journal2018 MEDICAL TECHNOLOGIES NATIONAL CONGRESS (TIPTEKNO)en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US


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