Motor Imagery Signal Classification Using Constant-Q Transform for BCI Applications

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dc.authorid0000-0002-9174-0367en_US
dc.authorscopusid57206481813en_US
dc.authorscopusid35781455400en_US
dc.contributor.authorBalim, Mustafa Alper
dc.contributor.authorHanilçi, Cemal
dc.contributor.authorAcir, Nurettin
dc.date.accessioned2022-04-21T06:04:37Z
dc.date.available2022-04-21T06:04:37Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractElectroencephalography (EEG) signals have been using for brain-computer interface applications for the last two decades. Motor imagery (MI) signals are one of the EEG signal types formed by imagining a limb's movement. Recently with the help of deep neural networks (DNN) for classifying MI signals using time-frequency (TF) features, considerable performance improvement has been reported. This paper proposes using a well-known TF representation technique called Constant-Q Transform (CQT) for the MI signal classification. Experiments conducted on BCI IV 2b dataset with DNN classifier using CQT spectrogram show that CQT outperforms traditional short-time Fourier transform (STFT) representation.en_US
dc.identifier.doi10.23919/EUSIPCO54536.2021.9616160en_US
dc.identifier.endpage1310en_US
dc.identifier.isbn978-908279706-0
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1306en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1941
dc.identifier.volume2021en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBalim, Mustafa Alper
dc.institutionauthorHanilçi, Cemal
dc.language.isoenen_US
dc.publisherEuropean Signal Processing Conference, EUSIPCOen_US
dc.relation.ispartof29th European Signal Processing Conference, EUSIPCO 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrain-computer interfaceen_US
dc.subjectConstant-Q transformen_US
dc.subjectElectroencephalographyen_US
dc.subjectMotor imageryen_US
dc.titleMotor Imagery Signal Classification Using Constant-Q Transform for BCI Applicationsen_US
dc.typeConference Objecten_US

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