A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis

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dc.authorid0000-0003-1186-3058en_US
dc.contributor.authorCinar, Salim
dc.contributor.authorAcır, Nurettin
dc.date.accessioned2021-03-20T20:13:59Z
dc.date.available2021-03-20T20:13:59Z
dc.date.issued2017
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractElectroEncephaloGram (EEG) gives information about the electrical characteristics of the brain. EEG can be used for various applications, such as diagnosis of diseases, neuroscience and Brain Computer Interface (BCI). Several artefacts sources can disturb the brain signals in EEG measurements. The signals caused by eye movements are the most important sources of artefacts that must be removed in order to obtain a clean EEG signal. During the removal of Ocular Artefacts (OAs), the preserve of the original EEG signal is one of the most important points to be taken into account. An ElectroOculoGram (EOG) reference signal is needed in order to remove OAs in some methods. However, long-term EOG measurements can disturb a subject. In this paper, a novel robust method is proposed in order to remove OAs automatically from EEG without EOG reference signal by combining Outlier Detection and Independent Component Analysis (OD-ICA). The OD-ICA method searches OA patterns in all components instead of a single component. Moreover, OD-ICA removes only OA patterns and preserves meaningful EEG signal. In this method, user intervention is not needed. These advantages make the method robust. The OD-ICA is tested on two real datasets. Relative Error (RE), Correlation Coefficient (CorrCoeff) and percentage of finding OA pattern are used for the performance test. Furthermore, three different methods are used as Outlier Detection (OD) methods. These are the Chauvenet Criterion, the Peirce's Criterion and the Adjusted Box Plot. The performance analysis is made between our proposed method and the method of zeroing the component with artefact. The experiment results show that the proposed OD-ICA method effectively removes OAs from EEG signals and is also successful in preserving the meaningful EEG signals during the removal of OAs. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2016.10.009en_US
dc.identifier.endpage44en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage36en_US
dc.identifier.urihttp://doi.org/10.1016/j.eswa.2016.10.009
dc.identifier.urihttps://hdl.handle.net/20.500.12885/976
dc.identifier.volume68en_US
dc.identifier.wosWOS:000388777700005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAcır, Nurettin
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIndependent component analysisen_US
dc.subjectOutlier detectionen_US
dc.subjectOcular artefacten_US
dc.subjectElectroencephalogram (EEG)en_US
dc.subjectElectroOculoGram (EOG)en_US
dc.titleA novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysisen_US
dc.typeArticleen_US

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