Detection of 2D and 3D Video Transitions Based on EEG Power. (7th September 2020)

• Record Type: Journal Article
• Authors: Manshouri, Negin; Melek, Mesut; Kayıkcıoglu, Temel
• Is Part Of: Computer journal. Volume 65:Number 2(2022)
• Page Start: 396
• Page End: 409
• Publication Date: 2020-09-07
• Subjects: EEG -- transition -- 2D to 3D -- anaglyph -- feature extraction -- classification -- hybrid; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa116
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain's power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, nine visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on short time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation, maximum (max) and Hjorth parameters to epochs indicating transition intervals. Ultimately, k -nearest neighbors, support vector machine and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady state, respectively.