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dc.contributor.authorMenguc, Engin Cemal
dc.contributor.authorAcır, Nurettin
dc.date.accessioned2021-03-20T20:14:03Z
dc.date.available2021-03-20T20:14:03Z
dc.date.issued2017
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.urihttps://hdl.handle.net/20.500.12885/988
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_US
dc.descriptionWOS:000426868700086en_US
dc.description.abstractIn this paper, a novel adaptive noise canceller (ANC) based on the quaternion valued least mean square algorithm (QLMS) is designed in order to remove electrooculography (EOG) artifacts from electroencephalography (EEG) recordings. The measurement real-valued EOG and EEG signals (FP1, FP2, AF3 and AF4) are first modeled as four-dimensional processes in the quaternion domain. The EOG artifacts are then removed from the EEG signals in the quaternion domain by using the ANC based on QLMS algorithm. The quaternion representation of these signals allows us to remove EOG artifacts from all channels at the same time instead of removing the EOG artifacts in each EEG recordings separately. The simulation results support the proposed approach.en_US
dc.description.sponsorshipIEEE Turkey Sect, Anatolian Scien_US
dc.language.isoengen_US
dc.publisherIeeeen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectQuaternion domainen_US
dc.subjectadaptive noise cancelleren_US
dc.subjectquaternion least mean squareen_US
dc.subjectEEG and EOG signalsen_US
dc.titleAn adaptive noise canceller based on QLMS algorithm for removing EOG artifacts in EEG recordingsen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentBTÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0003-1186-3058en_US
dc.contributor.institutionauthorAcır, Nurettin
dc.relation.journal2017 International Artificial Intelligence And Data Processing Symposium (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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