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.

Emotional arousal is a key component of a user's experience in immersive virtual reality (VR). Subjective and highly dynamic in nature, emotional arousal involves the whole body and particularly the brain. However, it has been difficult to relate subjective emotional arousal to an objective, neurophysiological marker - especially in naturalistic settings. We tested the association between continuously changing states of emotional arousal and oscillatory power in the brain during a VR roller coaster experience. We used novel spatial filtering approaches to predict self-reported emotional arousal from the electroencephalogram (EEG) signal of 38 participants. Periods of high vs. low emotional arousal could be classified with accuracies significantly above chance level. Our results are consistent with prior findings regarding emotional arousal in less naturalistic settings. We demonstrate a new approach to decode states of subjective emotional arousal from continuous EEG data in an immersive VR experience.