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Responses of functional brain networks while watching 2D and 3D videos: An EEG study

机译:在观看2D和3D视频时功能性大脑网络的回应:脑电图研究

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摘要

To our knowledge, the present study is the first to use electroencephalography (EEG) to investigate the reorganizations of functional brain networks when watching 2D and 3D videos. We aimed to reveal the underlying neural mechanisms that may cause different visual experiences from a brain network perspective. The EEG activities of 40 healthy participants were recorded while watching 2D and 3D videos. By constructing multiband functional brain networks, we analyzed the network efficiencies from both macro- and micro-scales. We observed: 1) at the macro-scale, higher global efficiency in beta (16 & ndash;32 Hz) and gamma (32 & ndash;63 Hz) networks in the 3D group, and 2) at the micro-scale, higher occipital and parietal efficiencies in beta and gamma networks in the 3D group, and higher frontal efficiency in the alpha (8 & ndash;16 Hz) network in the 2D group. Furthermore, using a small subset of functional connectivity features as inputs, a support vector machine classifier was used to classify the brain states induced by watching 2D and 3D videos. We achieved a satisfactory classification accuracy of 0.908 with an area of 0.96 under the receiver operating characteristic curve, using the top 18 features extracted from the beta band. Our findings are expected to uncover the underlying neural mechanisms related to different visual experiences during 2D and 3D video viewing from a brain network perspective.
机译:据我们所知,本研究是第一个使用脑电图(EEG)来调查函数脑网络时的重组,观看2D和3D视频。我们旨在揭示可能从脑网络角度造成不同视觉经验的潜在的神经机制。在观看2D和3D视频时录制了40名健康参与者的EEG活动。通过构建多频带功能性大脑网络,我们分析了宏观和微观尺度的网络效率。我们观察到:1)在宏观规模,较高的全球效率在β(16&Ndash; 32 Hz)和3D组中的伽马(32&Ndash; 63 Hz)网络,2)在微尺度,更高在3D组的Beta和伽马网络中的枕骨和痛苦效率,以及在2D组中的alpha(8– 16 Hz)网络中的额度效率较高。此外,使用作为输入的小功能连接特征的小子集,使用支持向量机分类器来分类通过观看2D和3D视频引起的脑状态。我们在接收器运行特性曲线下实现了0.908的令人满意的分类精度,面积为0.96,使用从β波段提取的前18个特征。我们的调查结果预计将在2D和3D视频观看期间揭示与不同视觉体验相关的潜在的神经机制,以及从脑网络的观点。

著录项

  • 来源
    《Biomedical signal processing and control》 |2021年第1期|102613.1-102613.10|共10页
  • 作者单位

    Shanghai Univ Qianweichang Coll Sch Commun & Informat Engn Shanghai Inst Adv Commun & Data Sci Shanghai 200065 Peoples R China;

    Shanghai Univ Qianweichang Coll Sch Commun & Informat Engn Shanghai Inst Adv Commun & Data Sci Shanghai 200065 Peoples R China;

    Shanghai Univ Shanghai Film Acad Dept Film & Televis Yanchang 149 Shanghai Peoples R China|Shanghai Univ Shanghai Engn Res Ctr Mot Picture Special Effects Yanchang 149 Shanghai Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Functional brain network; Phase locking value (PLV); EEG; 2D and 3D videos;

    机译:功能性大脑网络;阶段锁定值(PLV);eeg;2D和3D视频;

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