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.

中文翻译：

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基于EEG Power的2D和3D视频转换检测

尽管3D技术有着悠久而广泛的历史，但它最近吸引了研究人员的注意。这项技术因其创造的真实感受而成为年轻人关注的焦点。人们由于眼睛结构而将环境视为3D。在这项研究中，假设人们在困倦的时刻失去了对深度的感知，并且从3D视觉突然过渡到2D视觉。关于这些过渡，EEG信号分析方法用于2D和3D脑信号的深入和全面分析。在这项研究中，准备了随机2D和3D片段的单流浮雕视频。观看了单个视频后，将对获得的EEG录音进行两次不同的分析：涉及关键过渡（过渡状态）的部分以及仅2D对3D或3D对2D部分（稳态）的状态分析。这项研究的主要目的是观察脑信号在2D和3D转换中的行为变化。为了阐明立体图像视频在2D到3D（2D_3D）和3D到2D（3D_2D）转换中人脑的功率谱密度（PSD）的影响，准备了9个视觉健康的个体进行这项开创性研究的测试。考虑了基于短时傅立叶变换（STFT）的频谱图，以评估跃迁或稳态的每个EEG通道中的功率谱分析。因此，在2D和3D过渡方案中，将识别出代表EEG频带和脑叶的重要通道。要对2D和3D过渡进行分类，选择代表PSD的最大差异的主频带和时间间隔。之后，通过将统计偏差（例如标准偏差，最大值（最大值）和Hjorth参数）应用于指示过渡间隔的时期来选择有效特征。最终，使用k最近邻，支持向量机和线性判别分析（LDA）算法对2D_3D和3D_2D过渡进行分类。额叶，颞叶和部分顶叶显示2D_3D和3D_2D过渡，分类成功率高。总体而言，发现Hjorth参数和LDA算法在过渡状态和稳态下的分类成功率分别为71.11％和77.78％。