Ballı, TuğçeBulut, CemBalli, TugceYetkin, E. Fatih2023-10-192023-10-19202301300-18841304-4915https://doi.org/10.17341/gazimmfd.9788951197890https://hdl.handle.net/20.500.12469/5217Brain-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.eninfo:eu-repo/semantics/openAccessBCIEEGband powerfeature selectionclassificationArticle23972407438WOS:00097487600003410.17341/gazimmfd.9788952-s2.0-85153856204Q4Q2https://search.trdizin.gov.tr/yayin/detay/1197890