EEG-based mental workload estimation 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.accessioned2024-06-13T20:17:51Z
dc.date.available2024-06-13T20:17:51Z
dc.date.issued2023
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Psikoloji Bölümü
dc.description.abstractThe amount of mental capacity required by individuals to complete any task is defined as mental workload. It is important to determine the appropriate level in order not to impose too much mental workload on individuals or not to create unnecessary human resources for the completion of a task. Multiple Sclerosis (MS) is an autoimmune neurodegenerative central nervous system disease that activates the acquired and innate immune systems due to the interaction between genetic and environmental factors and manifests itself with different neurological symptoms. This study aims to classify the mental workload level of MS patients as low, medium, or high from EEG signals during cognitive tasks in computer and virtual reality environments and to compare them with a healthy group performing the same tasks. In this study, the mental workload level of 45 volunteers is estimated by using EEG signals and NASA-Raw Task Load Index questionnaire results in 3 cognitive tasks in computer and virtual reality environments. The three-level mental workload classification accuracy in MS patients with the Support Vector Machine classifier is 96.08% and 94.12% for computer and virtual reality environments, respectively. For healthy volunteers, classification accuracy is 95.24% and 94.05% in computer and virtual reality environments, respectively. In the study, mental workload research was conducted for the first time from EEG signals of MS patients obtained during cognitive tasks in computer and virtual reality environments.
dc.identifier.doi10.1007/s11760-023-02547-6
dc.identifier.endpage3301
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85153083850
dc.identifier.scopusqualityQ2
dc.identifier.startpage3293
dc.identifier.urihttps://doi.org/10.1007/s11760-023-02547-6
dc.identifier.urihttps://hdl.handle.net/11501/1114
dc.identifier.volume17
dc.identifier.wosWOS:000971389300005
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKeskinkılıç, Cahit
dc.institutionauthorid0000-0003-3799-4427
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMultiple Sclerosis
dc.subjectMental Workload
dc.subjectVirtual Reality
dc.subjectEEG
dc.subjectSupport Vector Machine
dc.titleEEG-based mental workload estimation of multiple sclerosis patients
dc.typeArticle

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