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dc.contributor.authorOzel, Pinar
dc.contributor.authorAkan, Aydin
dc.contributor.authorYilmaz, Bulent
dc.date.accessioned2021-08-23T07:29:50Z
dc.date.available2021-08-23T07:29:50Z
dc.date.issued2017en_US
dc.identifier.isbn978-1-5386-0633-9
dc.identifier.urihttps://hdl.handle.net/20.500.12573/924
dc.description.abstractElectrophysiological data processing can take place both in time and in frequency domains as well as in the joint time-frequency domain. Short Time Fourier Transform and Wavelet Transform are commonly used time-frequency analysis methods. The limitations of these methods initiated the use of methods such as synchrosqueezing and multivariate synchrosqueezing methods. In our proposed method 88.9%, 77.8%, 80.6% accuracy rates were obtained respectively for the valence, activation and dominance parameters using and multivariate synchrosqueezing methods and support vector machines(SVM) which yields better results than most of the other methods mentioned in the literature.en_US
dc.description.sponsorshipIEEE Turkey Secten_US
dc.language.isoengen_US
dc.publisherIEEE345 E 47TH ST, NEW YORK, NY 10017 USAen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmultivariate sychrosqueezing transformen_US
dc.subjectemotion recognitionen_US
dc.subjectEEGen_US
dc.titleEmotion Recognition Classification in EEG Signals Using Multivariate Synchrosqueezing Transformen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.contributor.institutionauthorYilmaz, Bulent
dc.relation.journal2017 MEDICAL TECHNOLOGIES NATIONAL CONGRESS (TIPTEKNO)en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US


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