Responses of functional brain networks while watching 2D and 3D videos: An EEG study. (July 2021)

• Record Type: Journal Article
• Authors: Yu, Minchang; Li, Yingjie; Tian, Feng
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Functional brain network -- Phase locking value (PLV) -- EEG -- 2D and 3D videos; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102613
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Watching 3D video caused higher global efficiency in brain networks compared to watching 2D video. Regional efficiencies of brain networks differ between 2D and 3D video watching. Classify brain states during the 2D and 3D video watching using functional connectivity features. 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.