Lukas Gehrke

Designing immersion is the key challenge in virtual reality; this challenge has driven advancements in displays, rendering and recently, haptics. To increase our sense of physical immersion, for instance, vibrotactile gloves render the sense of touching, while electrical muscle stimulation (EMS) renders forces. Unfortunately, the established metric to assess the effectiveness of haptic devices relies on the user’s subjective interpretation of unspecific, yet standardized, questions. Here, we explore a new approach to detect a conflict in visuo-haptic integration (e.g., inadequate haptic feedback based on poorly configured collision detection) using electroencephalography (EEG). We propose analyzing event-related potentials (ERPs) during interaction with virtual objects. In our study, participants touched virtual objects in three conditions and received either no haptic feedback, vibration, or vibration and EMS feedback. To provoke a brain response in unrealistic VR interaction, we also presented the feedback prematurely in 25% of the trials. We found that the early negativity component of the ERP (so called prediction error) was more pronounced in the mismatch trials, indicating we successfully detected haptic conflicts using our technique. Our results are a first step towards using ERPs to automatically detect visuo-haptic mismatches in VR, such as those that can cause a loss of the user’s immersion.

Objective. Neural interfaces hold significant promise to implicitly track user experience. Their application in virtual and augmented reality (VR/AR) simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess. Approach. We present a complimentary metric based on a event-related potentials. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal’s cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe electroencephalography (EEG) and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience. Main results. Using prediction error negativity features, we classified VR glitches with 77% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success. Significance. Prediction error signatures from these sources reflect violations of user’s predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces.

Abstract— Objective. Mixed-Reality (XR) technologies promise a user experience (UX) that rivals the interactive experience with the real-world. The key facilitators in the design of such a natural UX are that the interaction has zero lag and that users experience no excess mental load. This is difficult to achieve due to technical constraints such as motion-to-photon latency as well as false-positives during gesture-based interaction. Methods. In this paper, we explored the use of physiological features to model the user’s intent to interact with a virtual reality (VR) environment. Accurate predictions about when users want to express an interaction intent could overcome the limitations of an interactive device that lags behind the intention of a user. We computed time-domain features from electroencephalography (EEG) and electromyography (EMG) recordings during a grab-and-drop task in VR and cross-validated a Linear Discriminant Analysis (LDA) for three different combinations of (1) EEG, (2) EMG and (3) EEG-EMG features. Results & Conclusion. We found the classifiers to detect the presence of a pre-movement state from background idle activity reflecting the users’ intent to interact with the virtual objects (EEG: 62% ± 10%, EMG: 72% ± 9%, EEG-EMG: 69% ± 10%) above simulated chance level. The features leveraged in our classification scheme have a low computational cost and are especially useful for fast decoding of users’ mental states. Our work is a further step towards a useful classification of users’ intent to interact, as a high temporal resolution and speed of detection is crucial. This facilitates natural experiences through zero-lag adaptive interfaces.

Achieving temporal synchrony between sensory modalities is crucial for natural perception of object interaction in virtual reality. While subjective questionnaires are currently used to evaluate users’ VR experiences, leveraging behavior and psychophysiological responses can provide additional insights.We investigated motion and ocular behavior as discriminators between realistic and unrealistic object interactions. Participants grasped and placed a virtual object while experiencing sensory feedback that either matched their expectations or occurred too early. We also explored visual-only feedback vs. combined visual and haptic feedback. Due to technological limitations, a condition with delayed feedback was added post-hoc.Gaze-based metrics revealed discrimination between high and low feedback realism. Increased interaction uncertainty was associated with longer fixations on the avatar hand and temporal shifts in the gaze-action relationship. Our findings enable real-time evaluation of users’ perception of realism in interactions. They facilitate the optimization of interaction realism in virtual environments and beyond.