Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3617
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dc.contributor.authorYesilkaya B.-
dc.contributor.authorGuren O.-
dc.contributor.authorBahar M.T.-
dc.contributor.authorTurhal L.N.-
dc.contributor.authorAkan A.-
dc.date.accessioned2023-06-16T15:01:48Z-
dc.date.available2023-06-16T15:01:48Z-
dc.date.issued2020-
dc.identifier.isbn9.78173E+12-
dc.identifier.urihttps://doi.org/10.1109/SIU49456.2020.9302223-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3617-
dc.description28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- 166413en_US
dc.description.abstractEmotion recognition is an effective analysis method used to increase the interaction between human-machine interface. EEG based emotion recognition studies based on brain signals are preferred in order to provide healthy results of emotion analysis experiments. In this study, emotion recognition analysis was performed in accordance with dimensional emotion modelling. Data cleaning was performed by applying the necessary filters on the recorded data. The feature vector was then created and the success rate was determined using support vector machines and classification methods such as K-nearest neighbour. © 2020 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEGEen_US
dc.subjectEmotionen_US
dc.subjectEmotion Recognitionen_US
dc.subjectEnsemble Classifieren_US
dc.subjectFeature Vectoren_US
dc.subjectInternational Affective Image Systemen_US
dc.subjectK-nearest neighbouren_US
dc.subjectSupport vector machinesen_US
dc.subjectNearest neighbor searchen_US
dc.subjectSpeech recognitionen_US
dc.subjectSupport vector machinesen_US
dc.subjectClassification methodsen_US
dc.subjectEffective analysisen_US
dc.subjectEmotion analysisen_US
dc.subjectEmotion modellingen_US
dc.subjectEmotion recognitionen_US
dc.subjectFeature vectorsen_US
dc.subjectHuman Machine Interfaceen_US
dc.subjectK-nearest neighboursen_US
dc.subjectSignal processingen_US
dc.titleEstimation of Emotion Status Using IAPS Image Data Seten_US
dc.title.alternativeIAPS Goruntu Veri Seti Kullanarak Duygu Durum Kestirimien_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/SIU49456.2020.9302223-
dc.identifier.scopus2-s2.0-85100311182en_US
dc.authorscopusid57206472210-
dc.authorscopusid57221814153-
dc.authorscopusid57221818938-
dc.authorscopusid35617283100-
dc.identifier.wosWOS:000653136100197en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1tr-
item.cerifentitytypePublications-
crisitem.author.dept05.06. Electrical and Electronics Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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