Summary: This study was conducted to reveal the effects of 2 Dimensional (2D) and 3 Dimensional (3D) videos on human brain waves. As is known, people see their environment as 3D due to their eye structure. In this study, we put forward the hypothesis that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Capturing this important moment is our main goal. In the thesis, the effects of 2D and 3D video watching using electroencephalography (EEG) brain signals were investigated. Power spectrum density (PSD) based on short time Fourier transform (STFT) was used to analyze the brain signals of 2D&3D video viewers. All EEG frequency bands were tested and features were obtained from the dominant frequency bands. Partial least squares regression (KeKKR), support vector machine (DVM) and linear discriminant analysis (LDA) classification algorithms were used to classify the EEG signals obtained during 2D and 3D video watching. Successful classification results were obtained by selecting the correct combinations of effective channels representing the brain regions. Key Words: EEG, 2D and 3D video, Feature extraction, Classification, Power spectrum density |