Abstract
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 (16–32 Hz) and gamma (32–63 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 (8–16 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.
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