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Classification of motor imagery eeg signals for using in neuro-rehabilitation applications

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dc.contributor.authorBAŞPINAR, ULVİ
dc.contributor.authorsBAŞPINAR U., Taştan Y., Bulut Okay B.
dc.date.accessioned2022-12-22T13:24:34Z
dc.date.available2022-12-22T13:24:34Z
dc.date.issued2022-10-21
dc.description.abstractBrain computer interfaces which are developed for the rehabilitation systems decode motor imagery EEG signals to control external devices. However, the extraction of the features from the EEG imagery signals and classification of it is an important problem. In this paper, common spatial pattern analysis, which is widely used in motor image applications, was preferred for getting features. As a classifier, the accuracy performances of Artificial neural network (%93), Convolutional Neural Network (%91), Support Vector Machine (%84) and K- Nearest Neighbour Algorithm (%90) were compared. As a result of the comparison, Artificial Neural Network method was the most successful classifier with %93.9 accuracy.
dc.identifier.citationBAŞPINAR U., Taştan Y., Bulut Okay B., \"Classification of Motor Imagery EEG Signals for Using in Neuro-Rehabilitation Applications\", 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), Antalya, Türkiye, 07 Eylül 2022
dc.identifier.doi10.1109/asyu56188.2022.9925366
dc.identifier.endpage4
dc.identifier.startpage1
dc.identifier.urihttps://ieeexplore.ieee.org/document/9925366
dc.identifier.urihttps://hdl.handle.net/11424/283878
dc.language.isoeng
dc.relation.ispartof2022 Innovations in Intelligent Systems and Applications Conference (ASYU)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMotor Imagery EEG
dc.subjectcommon spatial pattern
dc.subjectdeep learning
dc.subjectmachine learning
dc.subjectclassification
dc.titleClassification of motor imagery eeg signals for using in neuro-rehabilitation applications
dc.typearticle
dspace.entity.typePublication
local.avesis.idfb7972bd-c89f-4687-859e-6c96bcb1f301
relation.isAuthorOfPublication871cff99-4107-488b-bd17-6370f89e619b
relation.isAuthorOfPublication.latestForDiscovery871cff99-4107-488b-bd17-6370f89e619b

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