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Bodily signals can complement subjective and behavioral measures to analyze human factors, such as user engagement or stress, when interacting with virtual reality (VR) environments. Enabling widespread use of (also the real-time analysis) of bodily signals in VR applications could be a powerful method to design more user-centric, personalized VR experiences. However, technical and scientific challenges (e.g., cost of research-grade sensing devices, required coding skills, expert knowledge needed to interpret the data) complicate the integration of bodily data in existing interactive applications. This paper presents the design, development, and evaluation of an open-source software framework named Excite-O-Meter. It allows existing VR applications to integrate, record, analyze, and visualize bodily signals from wearable sensors, with the example of cardiac activity (heart rate and its variability) from the chest strap Polar H10. Survey responses from 58 potential users determined the design requirements for the framework. Two tests evaluated the framework and setup in terms of data acquisition/analysis and data quality. Finally, we present an example experiment that shows how our tool can be an easy-to-use and scientifically validated tool for researchers, hobbyists, or game designers to integrate bodily signals in VR applications.

Immersive Virtual Reality (VR) shows promise for cognitive diagnostics and rehabilitation as it can present individuals with cognitive impairment with realistic, life-like environments, and allows to precisely record behavioral performance to infer indicators of cognitive processes. The aims of our study were to (1) determine the feasibility in using immersive VR in individuals with acquired brain injury, and detect limits in its applicability, and (2) estimate the extent to which the immersion, sense of presence, usability and general motivational aspects, and side effects affect users’ experience. To that end, a novel VR task, the immersive Virtual Memory Task (imVMT), was developed and applied to measure the spatial memory. Preliminary data will be discussed with a focus on feasibility.

The experimental features of Virtual Reality (VR) applications in clinical neuropsychology are only inadequately captured by classical psychometric quality criteria, warranting an extended paradigm evaluation framework. Here we propose such a framework by means of a multidimensional checklist (VR-Check). Evaluation dimensions encompass cognitive domain specificity, ecological relevance, technical feasibility, user feasibility, user motivation, task adaptability, performance quantification, immersive capacities, training feasibility, and predictable pitfalls. Assessment along these VR-Check dimensions enables a systematic and comparative evaluation of neuropsychological VR applications, as we illustrate here for the exemplary case of assessing spatial cognition with immersive VR.

Emotional arousal (EA) denotes a heightened state of activation that has both subjective and physiological aspects. The neurophysiology of subjective EA, among other mind-brain-body phenomena, can best be tested when subjects are stimulated in a natural fashion. Immersive virtual reality (VR) enables naturalistic experimental stimulation and thus promises to increase the ecological validity of research findings i.e., how well they generalize to real-life settings. In this study, 45 participants experienced virtual rollercoaster rides while their brain activity was recorded using electroencephalography (EEG). A Long Short-Term Memory (LSTM) recurrent neural network (RNN) was then trained on the alpha-frequency (8-12 Hz) component of the EEG signal (input) and the retrospectively acquired continuous reports of subjective EA (target). With the LSTM-based model, subjective EA could be predicted significantly above chance level. This demonstrates a novel EEG-based decoding approach for subjective states of experience in naturalistic research designs using VR.

A central question in neuroscience is how the brain reacts to real world sensory stimuli. Naturalistic and complex (e.g. movie) stimuli are increasingly used in empirical research but their analysis often relies on considerable human efforts to label or extract stimulus features. Here we present data-driven analysis strategies that help to obtain interpretable results from multisubject neuroimaging data when complex movie stimuli are used. These analyses a) enable localization and visualization of brain activity using standard statistical parametric maps in the subspace of brain activity shared between subjects and b) facilitate interpretation of intersubject correlations. We show experimental results obtained from 50 subjects.