With the recent advancements in the field of virtual reality, occupants can now be immersed into virtual built environments, where they have the ability to visualize and interact with the environment. The various challenges are supported by lessons learned from a pilot study, which focused on reflecting the initial validation methodology by analyzing the effect of different stimuli on user engagement.

In doing so, we provide an overview of issues and potential validation directions as an entry point for further research. In this article, we identify the different challenges and potential validation methodologies that affect the analysis of multisensory cues on user engagement. Yet, measuring and analyzing the actual effect of cues is hard as it spans numerous interconnected issues. The level of engagement spans the general level of presence in an environment, as well as the specific emotional response to a set trigger. Yet, designing effective techniques requires careful analysis of the effect of different cues on user engagement. Such techniques can be used for enhancing engagement in immersive systems. In this article, we report on challenges and potential methodologies to support the design and validation of multisensory techniques. 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. 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. 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.įurthermore, 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. By constructing multiband functional brain networks, we analyzed the network efficiencies from both macro- and micro-scales. The EEG activities of 40 healthy participants were recorded while watching 2D and 3D videos. We aimed to reveal the underlying neural mechanisms that may cause different visual experiences from a brain network perspective. 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.

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