The classification of eye state by using kNN and MLP classification models according to the EEG signals

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dc.contributor.authorSabancı, Kadir
dc.contributor.authorKoklu, Murat
dc.date.accessioned2020-03-26T18:59:43Z
dc.date.available2020-03-26T18:59:43Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractWhat is widely used for classification of eye state to detect humans cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for Knowledge Analysis) program is used for classification of eye state. Classification success was calculated by using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models. The obtained success of classification methods were compared. The classification success rates were calculated for various number of neurons in the hidden layer of a multilayer perceptron neural network model. The highest classification success rate have been obtained when the number of neurons in the hidden layer was equal to 7. And it was 56.45%. The classification success rates were calculated with k-nearest neighbors algorithm for different neighbourhood values. The highest success was achieved in the classification made with kNN algorithm. In kNN models, the success rate for 3 nearest neighbor were calculated as 84.05%.en_US
dc.identifier.citationSabancı, K., Koklu, M. (2015). The Classification of Eye State by Using KNN and MLP Classification Models According to the EEG Signals. International Journal of Intelligent Systems and Applications in Engineering, 3(4), 127-130.
dc.identifier.endpage130en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue4en_US
dc.identifier.startpage127en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TVRrek1ESTJOZz09
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31601
dc.identifier.volume3en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectEEG signalsen_US
dc.subjecteye stateen_US
dc.subjectweka
dc.subjectmultilayer perceptron
dc.subjectkNN classifier
dc.titleThe classification of eye state by using kNN and MLP classification models according to the EEG signalsen_US
dc.typeArticleen_US

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