Classification of Motor Imaginary Signals Using Stacked Autoencoders

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dc.authorid0000-0002-6685-758Xen_US
dc.contributor.authorBalım, Mustafa Alper
dc.contributor.authorAcır, Nurettin
dc.date.accessioned2021-03-20T20:26:50Z
dc.date.available2021-03-20T20:26:50Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413en_US
dc.description.abstractMotor imagery signals are one of the electroencephalography signals that occurs by subjects imagining the movement of a body limb and they used in brain computer interface applications. With the use of deep neural networks in motor imagery signal classification, classification performance has increased. Deep belief networks are one of the networks that increase performance. In this study, using a stacked autoencoders and BCI IV 2b dataset, a deep belief network and education method with more robust and higher performance is proposed. Average classification performance is obtained as 80.44%. © 2020 IEEE.en_US
dc.identifier.doi10.1109/SIU49456.2020.9302470en_US
dc.identifier.isbn9781728172064
dc.identifier.scopus2-s2.0-85100293998en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttp://doi.org/10.1109/SIU49456.2020.9302470
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1283
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBalım, Mustafa Alper
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.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectbrain computer interfaceen_US
dc.subjectdeep belief networksen_US
dc.subjectmotor imagery signal calssificationen_US
dc.subjectstacked autoencodersen_US
dc.titleClassification of Motor Imaginary Signals Using Stacked Autoencodersen_US
dc.title.alternativeHayali Motor Isaretlerin Yigilmis Otokodlayicilar Kullanilarak Siniflandirilmasien_US
dc.typeConference Objecten_US

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