Classification of 2D and 3D videos based on EEG waves

The purpose of this paper is to analyze the electroencephalography (EEG) signals of human brain during the 2D and 3D video watching. Analysis of the visual 2D and 3D motion pictures is meaningful and could be used in brain-computer interface and maybe detection of drowsiness. A total of eight healthy subjects consist of three women and five men that were in their twenties attended in the clinical trial. Subjects were asked to watch 2D and 3D of SAW video, by using 3D polarized passive glasses (cinema type), After data pre-processing, the features are extracted from the ½-sec epochs of the EEG using Fast Fourier Transform (FFT) and then classified by Support Vector Machines (SVM) and Linear discriminant analysis (LDA) algorithms. Results of classification on the test data show that the model can successfully classify the cases with an accuracy of %76.09 for the first classifier and %71.84 for the second one.