Comparative classification performances of filter model feature selection algorithms in EEG based brain computer interface system

dc.contributor.authorBallı, Tuğçe
dc.contributor.authorBalli, Tugce
dc.contributor.authorYetkin, E. Fatih
dc.date.accessioned2023-10-19T15:11:47Z
dc.date.available2023-10-19T15:11:47Z
dc.date.issued2023
dc.department-temp[Bulut, Cem] Istanbul Univ Cerrahpasa, Dept Comp Engn, TR-34320 Istanbul, Turkiye; [Balli, Tugce; Yetkin, E. Fatih] Kadir Has Univ, Management Informat Syst Dept, TR-34083 Istanbul, Turkiyeen_US
dc.description.abstractBrain-computer interface (BCI) systems enable individuals to use a computer or assistive technologies such as a neuroprosthetic arm by translating their brain electrical activity into control commands. In this study, the use of filter-based feature selection methods for design of BCI systems is investigated. EEG recordings obtained from a BCI system designed for the control of a neuroprosthetic device are analyzed. Two feature sets were created; the first set was band power features from six main frequency bands (delta (1.0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-25 Hz), high-beta (25-30Hz) and gamma (30-50 Hz)) and the second set was band power features from ten frequency sub-bands (delta (1-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-12 Hz), beta1 (12-15 Hz), beta2 (15-18 Hz), beta3 (18-25 Hz), gamma1 (30-35 Hz), gamma2 (35-40 Hz), gamma3 (40-50 Hz)). Ten filter-based feature selection methods are investigated along with linear discriminant analysis, random forests, decision tree and support vector machines algorithms. The results indicate that feature selection methods leads to a higher classification accuracy and eigen value centrality (Ecfs) and infinite feature selection (Inffs) methods have consistently provided higher accuracy rates as compared to rest of the feature selection methods.en_US
dc.identifier.citation0
dc.identifier.doi10.17341/gazimmfd.978895en_US
dc.identifier.endpage2407en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85153856204en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage2397en_US
dc.identifier.trdizinidhttps://search.trdizin.gov.tr/yayin/detay/1197890en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.978895
dc.identifier.uri1197890
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5217
dc.identifier.volume38en_US
dc.identifier.wosWOS:000974876000034en_US
dc.identifier.wosqualityQ4
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBCIen_US
dc.subjectEEGen_US
dc.subjectband poweren_US
dc.subjectfeature selectionen_US
dc.subjectclassificationen_US
dc.titleComparative classification performances of filter model feature selection algorithms in EEG based brain computer interface systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication97c3a2d8-b41c-40fe-9319-e0f9fc8516eb
relation.isAuthorOfPublication.latestForDiscovery97c3a2d8-b41c-40fe-9319-e0f9fc8516eb

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