Classification of Brain Electrical Dynamics Measured with Response to Opposite Season Video Stimuli

Abstract

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.