EEG based environment classification during cognitive task of multiple sclerosis patients

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dc.contributor.authorŞaşmaz Karacan, Seda
dc.contributor.authorSaraoğlu, Hamdi Melih
dc.contributor.authorCanbaz Kabay, Sibel
dc.contributor.authorAkdağ, Gönül
dc.contributor.authorKeskinkılıç, Cahit
dc.contributor.authorTosun, Mustafa
dc.date.accessioned2024-06-13T20:16:05Z
dc.date.available2024-06-13T20:16:05Z
dc.date.issued2022
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Psikoloji Bölümü
dc.description4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- Ankara -- 9-11 June 2022
dc.description.abstractMultiple sclerosis (MS) is a neurodegenerative central nervous system disease in which the tissues in the brain, cerebellum, brain stem, and spinal cord are damaged as a result of the immune system disorder. The aim of this study is to classify the environment from the EEG signals recorded during the cognitive task in the computer and virtual reality environment of MS patients and healthy volunteers. Multilayer perceptron (MLP), k-nearest neighbors algorithm (kNN), and Support Vector Machine (SVM) classifiers' performances are compared using EEG signals during a cognitive task of 11 MS patients and 28 healthy volunteers. EEG signals of volunteers are separated into alpha, beta, gamma, delta, and theta subbands with Wavelet Daubechies (db2). Spectral and statistical features of the subbands are extracted. The most important features are determined by the Recursive Feature Elimination (RFE) algorithm. Training and testing data are separated by Leave-One-Out Cross-Validation. While the best environment classification for healthy volunteers is 91.07% accuracy with the SVM classifier, the best classification performance for volunteers with MS is 95.45% accuracy with the kNN classifier.
dc.identifier.doi10.1109/HORA55278.2022.9799938
dc.identifier.isbn9781665468350
dc.identifier.scopus2-s2.0-85133975807
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/HORA55278.2022.9799938
dc.identifier.urihttps://hdl.handle.net/11501/1017
dc.indekslendigikaynakScopus
dc.institutionauthorKeskinkılıç, Cahit
dc.institutionauthorid0000-0003-3799-4427
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEEG
dc.subjectkNN
dc.subjectMLP
dc.subjectMultiple Sclerosis
dc.subjectSVM
dc.subjectVirtual Reality (VR)
dc.titleEEG based environment classification during cognitive task of multiple sclerosis patients
dc.typeConference Object

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