EEG Based Environment Classification During Cognitive Task of Multiple Sclerosis Patients

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dc.authorscopusid57215428333
dc.authorscopusid22836208800
dc.authorscopusid23984980500
dc.authorscopusid55958324000
dc.authorscopusid6507010051
dc.authorscopusid34979614700
dc.contributor.authorKaracan, S.S.
dc.contributor.authorSaraoglu, H.M.
dc.contributor.authorKabay, S.C.
dc.contributor.authorAkdag, G.
dc.contributor.authorKeskinkilic, C.
dc.contributor.authorTosun, M.
dc.date.accessioned2024-06-13T20:16:05Z
dc.date.available2024-06-13T20:16:05Z
dc.date.issued2022
dc.departmentİstanbul Gedik Üniversitesi
dc.description4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- -- 180434
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. © 2022 IEEE.
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.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
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.subjectBiomedical signal processing
dc.subjectBrain
dc.subjectDiscrete wavelet transforms
dc.subjectNearest neighbor search
dc.subjectNeurodegenerative diseases
dc.subjectStatistical methods
dc.subjectVirtual reality
dc.subjectCognitive task
dc.subjectEEG signals
dc.subjectEnvironment classification
dc.subjectHealthy volunteers
dc.subjectK Nearest Neighbor (k NN) algorithm
dc.subjectMultilayers perceptrons
dc.subjectMultiple sclerosis
dc.subjectSupport vector machine classifiers
dc.subjectSupport vectors machine
dc.subjectVirtual reality
dc.subjectSupport vector machines
dc.titleEEG Based Environment Classification During Cognitive Task of Multiple Sclerosis Patients
dc.typeConference Object

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