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dc.contributor.authorKöktürk, B.E.
dc.contributor.authorKaraçali, B.
dc.date.accessioned2021-02-12T18:43:53Z
dc.date.available2021-02-12T18:43:53Z
dc.date.issued2012
dc.identifier.isbn9781467300568
dc.identifier.urihttps://doi.org/10.1109/SIU.2012.6204600
dc.identifier.urihttps://hdl.handle.net/11147/9809
dc.description2012 20th Signal Processing and Communications Applications Conference, SIU 2012en_US
dc.description.abstractIn this study, the separation of the stimulus effects from the baseline was investigated in electroencephalography data recorded under different visual stimuli using quasi-supervised learning. The data feature vectors were constructed using independent component analysis and wavelet transform, and then, these feature vectors were separated using quasi-supervised learning. Experiment results showed that the EEG data of the stimuli can be separated using quasi-supervised learning. © 2012 IEEE.en_US
dc.language.isoturen_US
dc.relation.isversionof10.1109/SIU.2012.6204600en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectroencephalogramen_US
dc.subjectindependent component analysisen_US
dc.subjectquasi-supervised learningen_US
dc.subjectwavelet transformen_US
dc.titleAutomated labeling of electroencephalography data using quasi-supervised learning [Elektroensefalografi? veri?leri?ni?n yari- güdümlü ö?renme i?le otomati?k olarak i?şaretlenmesi?]en_US
dc.typeconferenceObjecten_US
dc.typeconferenceObjecten_US
dc.relation.journal2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedingsen_US
dc.contributor.departmentIzmir Isntitute of Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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