EEG-based finger movement classification with intrinsic time-scale decomposition

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dc.authorid0000-0003-0978-9653
dc.authorid0000-0002-3087-541X
dc.contributor.authorDegirmenci, Murside
dc.contributor.authorYuce, Yilmaz Kemal
dc.contributor.authorPerc, Matjaz
dc.contributor.authorIsler, Yalcin
dc.date.accessioned2026-01-24T12:29:27Z
dc.date.available2026-01-24T12:29:27Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractIntroduction Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.Methods In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.Results As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.Discussion When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).
dc.description.sponsorshipJavna Agencija za Raziskovalno Dejavnost RS10.13039/501100004329
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.3389/fnhum.2024.1362135
dc.identifier.issn1662-5161
dc.identifier.pmid38505099
dc.identifier.scopus2-s2.0-85188066140
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fnhum.2024.1362135
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5381
dc.identifier.volume18
dc.identifier.wosWOS:001186673700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Human Neuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectbrain-computer interfaces (BCIs)
dc.subjectelectroencephalogram (EEG)
dc.subjectfeature reduction
dc.subjectmachine learning
dc.subjectfinger movements (FM) classification
dc.subjectintrinsic time-scale decomposition (ITD)
dc.titleEEG-based finger movement classification with intrinsic time-scale decomposition
dc.typeArticle

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