Classification of finger movements using statistically significant time-domain EEG features

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dc.authorid0000-0002-2150-4756
dc.authorid0000-0003-0978-9653
dc.contributor.authorDegirmenci, Murside
dc.contributor.authorYuce, Yilmaz Kemal
dc.contributor.authorIsler, Yalcin
dc.date.accessioned2026-01-24T12:29:18Z
dc.date.available2026-01-24T12:29:18Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractPurpose: The accurate decoding of individual finger movements is pivotal for advanced prosthetic control. In this study, it is aimed to show the effectiveness statistically significant time-domain features based on ANOVA in the classification of EEG signals of finger movements. Theory and Methods: We tried to differentiate the finger movements EEG signals from the dataset which is an open-available and large motor imaginary EEG dataset. The time domain features were calculated from 21 channels of EEG signals of 8 healthy subjects. The effectiveness of two different feature selection methods which are statistically significance-based feature selection (ANOVA) and Principal Component Analysis (PCA) were investigated to discriminate five finger movements and no motor imagery task condition (NoMT). The feature vectors which including all time domain features, ANOVA-selected time domain features, PCA-selected time domain features, and both ANOVA- and PCA-selected time-domain features were applied to 8 different classifiers. The proposed approaches were investigated for both subject-dependent and subject-independent conditions. Results: The highest classification testing accuracy of 35.8% obtained using ANOVA-selected time-domain features with SVM classifier in subject-independent analysis; whereas, in subject-dependent analysis for 8 subjects, the highest classification testing accuracies achieved between 33.3%-57.5% using ANOVA-selected time-domain features for SVM classifier respectively. Conclusion: The results show that the ANOVA-based feature selection method improves the classification performances. The performances of the subject-dependent classifications are higher than the performance of the subject-independent classifications. Therefore, the subject-dependent results of the proposed model have a big potential, which may pave the way for the design of advanced personalized hand prostheses.
dc.identifier.doi10.17341/gazimmfd.1241334
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85185220699
dc.identifier.scopusqualityQ2
dc.identifier.trdizinid1257919
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.1241334
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1257919
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5257
dc.identifier.volume39
dc.identifier.wosWOS:001363134900006
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isotr
dc.publisherGazi Univ, Fac Engineering Architecture
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectFinger movement classification
dc.subjectElectroencephalogram signals]
dc.subjectMachine learning
dc.subjectStatistically significance
dc.titleClassification of finger movements using statistically significant time-domain EEG features
dc.title.alternativeİstatistiksel anlamlı zaman alanı EEG özniteliklerinden el parmak hareketlerinin sınıflandırılması
dc.typeArticle

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