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dc.contributor.authorAltindis, Fatih
dc.contributor.authorYilmaz, Bulent
dc.contributor.authorBorisenok, Sergey
dc.contributor.authorIcoz, Kutay
dc.date.accessioned2021-01-20T07:28:26Z
dc.date.available2021-01-20T07:28:26Z
dc.date.issued01.01.2021en_US
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2020.102196
dc.identifier.urihttps://hdl.handle.net/20.500.12573/466
dc.descriptionThis study was supported by Abdullah Gul University Scientific Research Projects Coordination Department. Project No: TOA-2015-31.en_US
dc.description.abstractTopological data analysis (TDA) methods have become appealing in EEG signal processing, because they may help the scientists explore new features of complex and large amount of data by simplifying the process from a geometrical perspective. Time delay embedding is a common approach to embed EEG signals into the state space. Parameters of this embedding method are variable and the structure of the state space can be entirely different depending on their selection. Additionally, extracted persistent homologies of the state spaces depend on filtration level and the number of points used. In this study, we showed how to adapt false nearest neighbor (FNN) test to find out the suitable/optimal time embedding parameters (i.e., time delay and embedding dimension) for EEG signals, and compared their effects on different types of artefacts and motor intention waves that are commonly used in brain-computer interfaces. We extracted and compared persistent homologies of state spaces that were reconstructed with four different sets of parameters. Later, the effect of filtration level on extracted persistent homologies was compared, and statistical significance levels were computed between leftand right-hand movement imaginations. Finally, computational cost of the discussed methods was found, and the adaptability of this method to a real-time application was evaluated. We demonstrated that the discussed parameters of the TDA approach were highly crucial to extract true topological features of the EEG signals, and the adapted testing approaches depicted the applicability of this approach on real-time analysis of EEG signals.en_US
dc.description.sponsorshipAbdullah Gul University Scientific Research Projects Coordination Department TOA-2015-31en_US
dc.language.isoengen_US
dc.publisherELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDen_US
dc.relation.isversionof10.1016/j.bspc.2020.102196en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTopological data analysisen_US
dc.subjectEEGen_US
dc.subjectBrain-Computer interfaceen_US
dc.subjectPersistent homologyen_US
dc.subjectFalse nearest neighborsen_US
dc.subjectMotor intention wavesen_US
dc.titleParameter investigation of topological data analysis for EEG signalsen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0002-0947-6166en_US
dc.contributor.authorID0000-0002-3891-935Xen_US
dc.identifier.volumeVolume: 63en_US
dc.relation.journalBIOMEDICAL SIGNAL PROCESSING AND CONTROLen_US
dc.relation.publicationcategoryMakale - Uluslararası - Editör Denetimli Dergien_US


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