Many illusion and interaction techniques in Virtual Reality (VR) rely on Hand Redirection (HR), which has proved to be effective as long as the introduced offsets between the position of the real and virtual hand do not noticeably disturb the user experience. Yet calibrating HR offsets is a tedious and time-consuming process involving psychophysical experimentation, and the resulting thresholds are known to be affected by many variables—limiting HR’s practical utility. As a result, there is a clear need for alternative methods that allow tailoring HR to the perceptual boundaries of individual users. We conducted an experiment with 18 participants combining movement, eye gaze and EEG data to detect HR offsets Below, At, and Above individuals’ detection thresholds. Our results suggest that we can distinguish HR At and Above from no HR. Our exploration provides a promising new direction with potentially strong implications for the broad field of VR illusions.

Hand redirection is effective so long as the introduced offsets are not noticeably disruptive to users. In this work we investigate the use of physiological and interaction data to detect movement discrepancies between a user's real and virtual hand, pushing towards a novel approach to identify discrepancies which are too large and therefore can be noticed. We ran a study with 22 participants, collecting EEG, ECG, EDA, RSP, and interaction data. Our results suggest that EEG and interaction data can be reliably used to detect visuo-motor discrepancies, whereas ECG and RSP seem to suffer from inconsistencies. Our findings also show that participants quickly adapt to large discrepancies, and that they constantly attempt to establish a stable mental model of their environment. Together, these findings suggest that there is no absolute threshold for possible non-detectable discrepancies; instead, it depends primarily on participants' most recent experience with this kind of interaction.

Steady-State-Visually-Evoked-Potential (SSVEP) Brain-Computer Interfaces (BCIs) make use of flickering stimuli to determine the target a user is looking at and select commands accordingly. Those types of BCI can be operated with little to no training, achieve high classification accuracies and are robust in application. A drawback of this approach is the reduced user comfort due to the constant flickering of the stimuli which can be annoying and tiring to look at. Existing studies addressing this issue try to make use of motion to disguise the oscillating patterns. However, this makes them look abstract and restricts the design of those applications as those patterns do not blend in to conventional user interfaces. In this work we introduce the concept of spinning icons to evoke SSVEPs. The icons are rotating in a certain frequency around their vertical axis and are supposed to appear more natural and be less stressing for the human eye. Furthermore this concept is not bound to any kind of abstract motion based pattern but rather supposed to work with any type of icon or image. The newly designed stimuli were evaluated in an application-oriented scenario and compared to standard and state-of-the-art movement-based SSVEP stimuli regarding the classification accuracy and experienced visual fatigue. The results show that the newly created stimuli performed equally well and partially even better in terms of classification accuracy and were rated throughout better concerning visual fatigue by the study participants. This work therefore lays the foundation for more comfortable SSVEP-BCIs which can be used with basically every icon or UI element spinning around their vertical axis.