Deep Learning on VR-Induced Attention

Some evidence suggests that virtual reality (VR) approaches may lead to a greater attentional focus than experiencing the same scenarios presented on computer monitors. The aim of this study is to differentiate attention levels captured during a perceptual discrimination task presented on two different viewing platforms, standard personal computer (PC) monitor and head-mounted-display (HMD)-VR, using a well-described electroencephalography (EEG)-based measure (parietal P3b latency) and deep learning-based measure (that is EEG features extracted by a compact convolutional neural network-EEGNet and visualized by a gradient-based relevance attribution method-DeepLIFT). Twenty healthy young adults participated in this perceptual discrimination task in which according to a spatial cue they were required to discriminate either a "Target" or "Distractor" stimuli on the screen of viewing platforms. Experimental results show that the EEGNet-based classification accuracies are highly correlated with the p values of statistical analysis of P3b. Also, the visualized EEG features are neurophysiologically interpretable. This study provides the first visualized deep learning-based EEG features captured during an HMD-VR-based attentional tas

Evaluating User Experience in Multisensory Meditative Virtual Reality: A Pilot Study

Virtual Reality (VR) is known for its ability to immerse users in a parallel universe. Accordingly, VR offers great potential for mindfulness therapy, especially in a post pandemic world. However, the extent to which our senses should be recruited to yield an optimal feeling of presence in the Virtual Environment (VE) remains unclear. This study investigates lived and perceived effects of adding auditory and motor components to VR experiences, through narration and head movements respectively. Twelve participants experienced four nature-based VR videos in a within-subjects research design. The study employed a mixed method approach of psychometric and neurophysiological measures. Results support a significant relationship between positive affect and presence. While statistical support was not obtained for the remaining relationships, this study provides a feasibility assessment of utilizing NeuroIS methods in evaluating immersive user experiences, along with qualitative insights that extend our understanding towards optimized VE designs

Enhanced attention using head-mounted virtual reality

Some evidence suggests that experiencing a given scenario using virtual reality (VR) may engage greater attentional resources than experiencing the same scenario on a 2D computer monitor. However, the underlying neural processes associated with these VR-related effects, especially those pertaining to current consumer-friendly head-mounted displays of virtual reality (HMD-VR), remain unclear. Here, two experiments were conducted to compare task performance and EEG-based neural metrics captured during a perceptual discrimination task presented on two different viewing platforms. Forty participants (20–25 years old) completed this task using both an HMD-VR and traditional computer monitor in a within-group, randomized design. Although Experiment I (n = 20) was solely behavioral in design, Experiment II (n = 20) utilized combined EEG recordings to interrogate the neural correlates underlying potential performance differences across platforms. These experiments revealed that (1) there was no significant difference in the amount of arousal measured between platforms and (2) selective attention abilities in HMD-VR environment were enhanced from both a behavioral and neural perspective. These findings suggest that the allocation of attentional resources in HMD-VR may be superior to approaches more typically used to assess these abilities (e.g., desktop/laptop/tablet computers with 2D screens)

An EEG-based evaluation for comparing the sense of presence between virtual and physical environments

The current study concerns the identification of possible differences in perception between the virtual and the real world in terms of the effect on brain activity. For this reason, an EEG device was used to capture participants' brain activity in different brain areas during their exposure to different virtual and real environments. The environments considered in this study portray a classroom environment with a scenario suitable for teacher training and professional development. The first aim of the experiment is to investigate if exposure to a virtual environment can affect motor, cognitive or other function of the users, and the second aim is to test if the graphics content and nature of such an environment can influence the user experience. During the study, the optimum duration of exposure in a virtual environment was also assessed by measuring the time that the brain needs to perceive and adapt to the new state. Our results, consisting of EEG data analyzed in 10 Regions of Interest (ROIs) and responses from an Igroup Presence questionnaire, indicated a significant difference in each brain area, especially in the frontal and occipital region, when a participant was exposed to a non-realistic virtual environment, compared to a realistic one, highlighting the impact of the selected virtual environment design. The results of the experiment can play an important role in defining the characteristics of optimal virtual environments for virtual reality-based training applications

Exploiting physiological changes during the flow experience for assessing virtual-reality game design.

Immersive experiences are considered the principal attraction of video games. Achieving a healthy balance between the game's demands and the user's skills is a particularly challenging goal. However, it is a coveted outcome, as it gives rise to the flow experience – a mental state of deep concentration and game engagement. When this balance fractures, the player may experience considerable disinclination to continue playing, which may be a product of anxiety or boredom. Thus, being able to predict manifestations of these psychological states in video game players is essential for understanding player motivation and designing better games. To this end, we build on earlier work to evaluate flow dynamics from a physiological perspective using a custom video game. Although advancements in this area are growing, there has been little consideration given to the interpersonal characteristics that may influence the expression of the flow experience. In this thesis, two angles are introduced that remain poorly understood. First, the investigation is contextualized in the virtual reality domain, a technology that putatively amplifies affective experiences, yet is still insufficiently addressed in the flow literature. Second, a novel analysis setup is proposed, whereby the recorded physiological responses and psychometric self-ratings are combined to assess the effectiveness of our game's design in a series of experiments. The analysis workflow employed heart rate and eye blink variability, and electroencephalography (EEG) as objective assessment measures of the game's impact, and self-reports as subjective assessment measures. These inputs were submitted to a clustering method, cross-referencing the membership of the observations with self-report ratings of the players they originated from. Next, this information was used to effectively inform specialized decoders of the flow state from the physiological responses. This approach successfully enabled classifiers to operate at high accuracy rates in all our studies. Furthermore, we addressed the compression of medium-resolution EEG sensors to a minimal set required to decode flow. Overall, our findings suggest that the approaches employed in this thesis have wide applicability and potential for improving game designing practices