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2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 -- 24 April 2019 through 26 April 2019, nofulltext# --- Atasoy, Mehmet Berkay (Arel Author), Birankar, Eyüp (Arel Author), Arıca, Şafak Abdullah (Arel Author), Duru, Dilek Göksel (Arel Author), 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.