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dc.contributor.authorAkan, Aydin
dc.contributor.authorAkkurt, Nihan
dc.contributor.authorMert, Ahmet
dc.date.accessioned2021-03-05T09:44:01Z
dc.date.available2021-03-05T09:44:01Z
dc.identifier.citationMert A., Akkurt N., Akan A., "EOG DENOISING USING EMPIRICAL MODE DECOMPOSITION AND DETRENDED FLUCTUATION ANALYSIS", 22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Türkiye, 23 - 25 Nisan 2014, ss.544-547
dc.identifier.otherav_9f549b55-3690-433a-94b5-57770211f8cd
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/106956
dc.identifier.urihttps://doi.org/10.1109/siu.2014.6830286
dc.description.abstractIn this study, a method is presented for the removal of electrooculogram (EOG) noise from electroencephalography (EEG) recordings by using recently proposed data driven approach called Empirical Mode Decomposition (EMD). The EMD represents the signal as a combination of Intrinsic Mode Functions (IMFs). It is an important problem to determine which IMFs belong to signal and noise in multi-component or noisy signals. Detrended Fluctuation Analysis (DFA) is a successful method to characterize non-stationary signals. In our approach, a threshold is determined from the DFA, and used to select the noise IMEs. Performance of the proposed method is demonstrated by means of computer simulations using noisy EEG signals.
dc.language.isoeng
dc.subjectTELEKOMÜNİKASYON
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectMühendislik
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectSinyal İşleme
dc.subjectMühendislik ve Teknoloji
dc.titleEOG DENOISING USING EMPIRICAL MODE DECOMPOSITION AND DETRENDED FLUCTUATION ANALYSIS
dc.typeBildiri
dc.contributor.departmentPiri Reis Üniversitesi , ,
dc.contributor.firstauthorID143374


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