TRDizin:
An Overview of Classification of Electrooculography (EOG) Signals by Machine Learning Methods

dc.contributor.authorCengiz TEPE
dc.contributor.authorMehmet Serhat ODABAŞ
dc.contributor.authorAlihan SUİÇMEZ
dc.date.accessioned2023-04-13T22:49:58Z
dc.date.available2023-04-13T22:49:58Z
dc.date.issued2022-03-01
dc.description.abstractThe distribution of the studies conducted between 2011-2021 in the fields of (Electrooculography) EOG and eye movements, EOG and wheelchair, EOG and eye angle, EOG and sleep state, EOG and mood estimation and EOG and game application was determined according to years, and the most cited studies were examined and presented. The study areas are listed as Eye Movement Classification, Wheelchair, Sleep state, Eye Angle, Mood State and Game Applications from the most to the least number of articles. When we examine in terms of the number of citations, they are listed as Sleeping state, Eye Movement Classification, Wheelchair, Eye Angle, Mood State and Game Applications, from the most to the least. In these studies, it has been tried to make the lives of people who have become disabled in various ways better by using the brain-computer interface with machine learning.
dc.identifier.citationTepe, C., Odabaş, M., Sui̇çmez, A. (2022). An Overview of Classification of Electrooculography (EOG) Signals by Machine Learning Methods. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 10(2), 330-338
dc.identifier.doi10.29109/gujsc.1130972
dc.identifier.eissn2147-9526
dc.identifier.endpage338
dc.identifier.issn
dc.identifier.issue2
dc.identifier.startpage330
dc.identifier.trdizin532960
dc.identifier.urihttps://search.trdizin.gov.tr/publication/detail/532960/an-overview-of-classification-of-electrooculography-eog-signals-by-machine-learning-methods
dc.identifier.urihttps://hdl.handle.net/20.500.12597/6311
dc.identifier.volume10
dc.language.isoeng
dc.relation.ispartofGazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleAn Overview of Classification of Electrooculography (EOG) Signals by Machine Learning Methods
dc.typeCOMPILATION
dspace.entity.typeTrdizin
local.indexed.atTrDizin
relation.isPublicationOfTrdizin89609171-c5eb-4068-aac3-3f9e148f18c5
relation.isPublicationOfTrdizin.latestForDiscovery89609171-c5eb-4068-aac3-3f9e148f18c5

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