dc.contributor.author | Bulut, Cem | |
dc.contributor.author | Balli, Tugce | |
dc.contributor.author | Yetkin, E. Fatih | |
dc.date.accessioned | 2023-10-19T15:11:47Z | |
dc.date.available | 2023-10-19T15:11:47Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 1300-1884 | |
dc.identifier.issn | 1304-4915 | |
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.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.language.iso | eng | 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.rights | info:eu-repo/semantics/openAccess | en_US |
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.identifier.startpage | 2397 | en_US |
dc.identifier.endpage | 2407 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.volume | 38 | en_US |
dc.department | N/A | en_US |
dc.identifier.wos | WOS:000974876000034 | en_US |
dc.identifier.doi | 10.17341/gazimmfd.978895 | en_US |
dc.identifier.scopus | 2-s2.0-85153856204 | en_US |
dc.institutionauthor | N/A | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.trdizinid | https://search.trdizin.gov.tr/yayin/detay/1197890 | en_US |
dc.khas | 20231019-WoS | en_US |