Comparative Classification Performances of Filter Model Feature Selection Algorithms in Eeg Based Brain Computer Interface System

dc.contributor.author Bulut, Cem
dc.contributor.author Ballı, Tuğçe
dc.contributor.author Balli, Tugce
dc.contributor.author Yetkin, Emrullah Fatih
dc.contributor.author Yetkin, E. Fatih
dc.contributor.other Business Administration
dc.contributor.other Management Information Systems
dc.date.accessioned 2023-10-19T15:11:47Z
dc.date.available 2023-10-19T15:11:47Z
dc.date.issued 2023
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, Turkiye en_US
dc.description.abstract Brain-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.citationcount 0
dc.identifier.doi 10.17341/gazimmfd.978895 en_US
dc.identifier.endpage 2407 en_US
dc.identifier.issn 1300-1884
dc.identifier.issn 1304-4915
dc.identifier.issue 4 en_US
dc.identifier.scopus 2-s2.0-85153856204 en_US
dc.identifier.scopusquality Q2
dc.identifier.startpage 2397 en_US
dc.identifier.trdizinid https://search.trdizin.gov.tr/yayin/detay/1197890 en_US
dc.identifier.uri https://doi.org/10.17341/gazimmfd.978895
dc.identifier.uri 1197890
dc.identifier.uri https://hdl.handle.net/20.500.12469/5217
dc.identifier.volume 38 en_US
dc.identifier.wos WOS:000974876000034 en_US
dc.identifier.wosquality Q4
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Gazi Univ, Fac Engineering Architecture en_US
dc.relation.ispartof Journal of The Faculty of Engineering and Architecture of Gazi University en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 3
dc.subject BCI en_US
dc.subject EEG en_US
dc.subject band power en_US
dc.subject feature selection en_US
dc.subject classification en_US
dc.title Comparative Classification Performances of Filter Model Feature Selection Algorithms in Eeg Based Brain Computer Interface System en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication
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