Automatic detection of cursor movements from the EEG signals via deep learning approach

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dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorPolat, Hasan
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2022-07-04T12:37:56Z
dc.date.available2022-07-04T12:37:56Z
dc.date.issued2020en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000629055500064
dc.description.abstractThe classification of motor imagery (MI) tasks is one of the key objectives of EEC-based brain-computer interface (BC!) systems. To ensure successful classification performance to BCI systems, researchers endeavor to extract appropriate features. However, these challenges are based on the conventional method. In this study, EEG signals related to MI tasks are classified using the convolutional neural network (CNN), which does not need a separate feature extraction. EEG records, which are generally evaluated as one-dimensional in machine learning problems, were taken into consideration as the image representation by using a novel method. The datasets were taken from a healthy subject. The subject was asked to move a cursor up and down on a computer screen, while his cortical potentials were taken. The EEG signals recorded over the 3.5-second time interval were evaluated for both the whole time and sub time intervals. Thus, the most effective time interval that has distinguishing features for EEG recordings related to different cursor movements was tried to be determined as well. As a result, it has been shown that the proposed model based on deep learning approach can successfully classify EEG signals related to cursor movements.en_US
dc.identifier.citationPolat, H. ve Özerdem, M.S. (2020, Sept 9-11). Automatic detection of cursor movements from the EEG signals via deep learning approach. [Proceedings Paper]. 5th International Conference on Computer Science and Engineering (UBMK). New York: IEEE. (pp. 327-332)en_US
dc.identifier.endpage332en_US
dc.identifier.isbn978-1-7281-7565-2
dc.identifier.scopusScopusIdYok
dc.identifier.scopusqualityN/A
dc.identifier.startpage327en_US
dc.identifier.urihttps://hdl.handle.net/11468/10101
dc.identifier.wosWOS:000629055500064
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherIEEE-Institute of Electrical Electronics Engineers INC.en_US
dc.relation.ispartof5th International Conference on Computer Science and Engineering (UBMK)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEEGen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectClassificationen_US
dc.titleAutomatic detection of cursor movements from the EEG signals via deep learning approachen_US
dc.titleAutomatic detection of cursor movements from the EEG signals via deep learning approach
dc.typeConference Objecten_US

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