Automatic Removal of Ocular Artefacts in EEG Signal by Using Independent Component Analysis and Artificial Neural Network

dc.authoridMenguc, Engin Cemal/0000-0002-0619-549X
dc.contributor.authorCInar, Salim
dc.contributor.authorMenguc, Engin Cemal
dc.contributor.authorAcir, Nurettin
dc.date.accessioned2024-11-07T13:24:05Z
dc.date.available2024-11-07T13:24:05Z
dc.date.issued2017
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.descriptionMedical Technologies National Congress (TIPTEKNO) -- OCT 12-14, 2017 -- TRABZON, TURKEY
dc.description.abstractOcular artefacts caused by eye movements can distort Electroencephalogram (EEG) recordings. It is important to obtain clean EEG signals in diagnosing and interpreting diseases. Meaningful EEG signals should not be distorted during the removal of artefacts. In this study, Independent Component Analysis and Artificial Neural Network were used together to remove ocular artefacts. The method was tested by using the real dataset. The Relative Error (RE) and Correlation Coefficient (CC) was used to test the performance of the method. Relative error = 0.227 0.229 and correlation coefficient = 0.941 0.088 was calculated in the performance analysis. According to the results, the proposed method is successful in removing ocular artefacts in EEG signals.
dc.description.sponsorshipIEEE Turkey Sect
dc.identifier.isbn978-1-5386-0633-9
dc.identifier.urihttps://hdl.handle.net/11480/13901
dc.identifier.wosWOS:000427649500044
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2017 Medical Technologies National Congress (Tiptekno)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectICA
dc.subjectremoval of arefacts
dc.subjectartificial neural network
dc.subjectkurtosis
dc.titleAutomatic Removal of Ocular Artefacts in EEG Signal by Using Independent Component Analysis and Artificial Neural Network
dc.typeConference Object

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