Zıt mevsim video uyaranlarına karşı ölçülen beyin elektriksel dinamiklerinin sınıflandırılması
Erişim
info:eu-repo/semantics/closedAccessTarih
2019Yazar
Atasoy, Mehmet BerkayBirankar, Eyüp
Arıca, Şafak Abdullah
Güney, Selen
Akbulut, Hüseyin
Achylov, Rahmet
Duru, Dilek Göksel
Duru, Adil Deniz
Üst veri
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In this study, it was aimed to classify the electrical signals recorded from human brain during different season (summer-winter) videos as stimuli. Data have been recorded using 14 channels EEG from four male participants. The power of delta, theta, alpha, beta and gamma frequency bands have been recorded and used to classify the collected data. Decision tree pre-processing method have been used to select the attributes of frequency bands and electrodes. To classify the data, support vector machines (SVM), linear discriminant analysis (LDA) and logistic regression (LR) machine learning algorithms have been used. It was found that it was separated %82.25 with SVM, %81 with LDA and %80.75 with LR. The results of three algorithms have shown similar scores. © 2019 IEEE.