Identifying and Removing Interference and Artifacts in Multifractal Signals With Application to EEG Signals

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dc.contributor.authorHbibi, Bechiren
dc.contributor.authorKhiari, Cyrineen
dc.contributor.authorWirsing, Karltonen
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorBaccar, Kamelen
dc.contributor.authorMami, Abdelkaderen
dc.date.accessioned2024-01-24T14:29:31Zen
dc.date.available2024-01-24T14:29:31Zen
dc.date.issued2023-10-18en
dc.description.abstractRecorded Electroencephalogram (EEG) signals are typically affected by interference and artifacts, which can both impact eye reading and computer analysis of the data. Artifacts are induced by physiological (noncerebral) activities of the patient, such as muscular activities of the eyes, or the heart, or the body, while interference may be of external or internal origin. External interference can be induced by electrical machines if the latter are in the same room as the patients, while internal interference can be caused by abnormal breathing, or body movement, or electrode malfunction, or headset movements. Interference may cause severe distortion of EEG signals, resulting in loss of some segments of brain signals, while artifacts are additive signals to brain signals. Therefore, in order to analyze the brain activity signals of a patient, we need to identify and eliminate interference and isolate artifacts. In this paper, we analyze the EEG signals that were recorded using a headset with fourteen channels placed on the heads of comatose patients at the National Institute of Neurology in Tunis, Tunisia. We identify the interference using a robust statistical method known as projection statistics and we separate the brain signals from the artifacts cited above by applying an independent component analysis method. Finally, we show the multifractal behavior of the EEG signals without interference by applying the wavelet leader method and analyze their properties using the singularity spectrum.en
dc.description.versionPublished versionen
dc.format.extentPages 119090-119105en
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2023.3325786en
dc.identifier.eissn2169-3536en
dc.identifier.issn2169-3536en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117646en
dc.identifier.volume11en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectartifactsen
dc.subjectinterferenceen
dc.subjectindependent component analysisen
dc.subjectprojection statisticsen
dc.subjectmultifractal analysisen
dc.subjectEEGlab toolboxen
dc.titleIdentifying and Removing Interference and Artifacts in Multifractal Signals With Application to EEG Signalsen
dc.title.serialIEEE Accessen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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