Motor imaginary task classification using statistically significant time-domain EEG features

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dc.authorid0000-0003-0978-9653
dc.authorid0000-0003-4023-0401
dc.contributor.authorDegirmenci, Murside
dc.contributor.authorYuce, Yilmaz Kemal
dc.contributor.authorIsler, Yalcin
dc.date.accessioned2026-01-24T12:29:01Z
dc.date.available2026-01-24T12:29:01Z
dc.date.issued2022
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description30th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2022 -- Safranbolu, TURKEY
dc.description.abstractMotor imaginary (MI) task classification based on electroencephalogram (EEG) signals is among the most common brain-computer interface (BCI) studies. One of the most widely used open-access datasets for this purpose is BCI Competition IV Dataset-IIa. In this data set, there are EEG data recorded during MI movements of the left hand, right hand, foot and tongue. In this study, four MI tasks were tried to be differentiated with the classifiers that are frequently used in the literature utilizing these 22-channel EEG data from a total of nine subjects. In addition, the effect of selecting statistically significant features from the features extracted on the dataset was investigated. These feature sets were differentiated using 11 different classification algorithms and 5-fold cross-validation. Each algorithm was tested 10 times to analyzed the repeatability of the results. As a result, classifier performances of %44.38 were obtained in the Ensemble classification Subspace Discriminant algorithm using all time-domain EEG features and %44.00 in the Linear Discriminant Analysis algorithm using only the features selected by ANOVA. Although the highest classifier performance seems to have decreased, it was observed that the feature selection process with ANOVA increased the performance in 6 classifiers, did not change the performance in 1 classifier, and decreased the performance in 4 classifiers. Accordingly, it was concluded that the method of selecting statistically significant features generally increased the classifier performance, but it was difficult to reach a general decision.
dc.description.sponsorshipIEEE,IEEE Turkey Sect,Bahcesehir Univ
dc.identifier.doi10.1109/SIU55565.2022.9864745
dc.identifier.isbn978-1-6654-5092-8
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85138685735
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU55565.2022.9864745
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5069
dc.identifier.wosWOS:001307163400084
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof2022 30th Signal Processing and Communications Applications Conference, Siu
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectEEG signals
dc.subjectmotor imaginary task classification
dc.subjectstatistical significance
dc.subjectfeature selection
dc.titleMotor imaginary task classification using statistically significant time-domain EEG features
dc.title.alternativeIstatistiksel olarak anlamli EEG zaman alani öznitelikleri ile motor hayali görev siniflandirilmasi
dc.typeConference Object

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