Recent advances in humanoid robot technologies have made it possible to inhabit a humanlike form located at a remote place. This allows the participant to interact with others in that space and experience the illusion that the participant is actually present in the remote space. Moreover, with these humanlike forms, it may be possible to induce a full-body ownership illusion, where the robot body is perceived to be one's own. We show that it is possible to induce the full-body ownership illusion over a remote robotic body with a highly robotic appearance. Additionally, our results indicate that even with nonmanual control of a remote robotic body, it is possible to induce feelings of agency and illusions of body ownership. Two established control methods, an SSVEP-based BCI and eye tracking, were tested as a means of controlling the robot's gesturing. Our experience and the results indicate that both methods are tractable for immersive control of a humanoid robot in a social telepresence setting.

Brain–computer interfaces (BCIs) are becoming more and more popular as an input device for virtual worlds and computer games. Depending on their function, a major drawback is the mental workload associated with their use and there is significant effort and training required to effectively control them. In this paper, we present two studies assessing how mental workload of a P300-based BCI affects participants' reported sense of presence in a virtual environment (VE). In the first study, we employ a BCI exploiting the P300 event-related potential (ERP) that allows control of over 200 items in a virtual apartment. In the second study, the BCI is replaced by a gaze-based selection method coupled with wand navigation. In both studies, overall performance is measured and individual presence scores are assessed by means of a short questionnaire. The results suggest that there is no immediate benefit for visualizing events in the VE triggered by the BCI and that no learning about the layout of the virtual space takes place. In order to alleviate this, we propose that future P300-based BCIs in VR are set up so as require users to make some inference about the virtual space so that they become aware of it, which is likely to lead to higher reported presence.

We have set up a brain-computer interface (BCI) to be used as an input device to a highly immersive virtual reality CAVE-like system. We have carried out two navigation experiments: three subjects were required to rotate in a virtual bar room by imagining left or right hand movement, and to walk along a single axis in a virtual street by imagining foot or hand movement. In this paper we focus on the subjective experience of navigating virtual reality “by thought,” and on the interrelations between BCI and presence.

Healthy participants are able to move forward within a virtual environment (VE) by the imagination of foot movement. This is achieved by using a brain-computer interface (BCI) that transforms thought-modulated electroencephalogram (EEG) recordings into a control signal. A BCI establishes a communication channel between the human brain and the computer. The basic principle of the Graz-BCI is the detection and classification of motor-imagery-related EEG patterns, whereby the dynamics of sensorimotor rhythms are analyzed. A BCI is a closed-loop system and information is visually fed back to the user about the success or failure of an intended movement imagination. Feedback can be realized in different ways, from a simple moving bar graph to navigation in VEs. The goals of this work are twofold: first, to show the influence of different feedback types on the same task, and second, to demonstrate that it is possible to move through a VE (e.g., a virtual street) without any muscular activity, using only the imagination of foot movement. In the presented work, data from BCI feedback displayed on a conventional monitor are compared with data from BCI feedback in VE experiments with a head-mounted display (HMD) and in a high immersive projection environment (Cave). Results of three participants are reported to demonstrate the proof-of-concept. The data indicate that the type of feedback has an influence on the task performance, but not on the BCI classification accuracy. The participants achieved their best performances viewing feedback in the Cave. Furthermore the VE feedback provided motivation for the subjects.

An experiment was conducted in a Cave-like environment to explore the relationship between physiological responses and breaks in presence and utterances by virtual characters towards the participants. Twenty people explored a virtual environment (VE) that depicted a virtual bar scenario. The experiment was divided into a training and an experimental phase. During the experimental phase breaks in presence (BIPs) in the form of whiteouts of the VE scenario were induced for 2 s at four equally spaced times during the approximately 5 min in the bar scenario. Additionally, five virtual characters addressed remarks to the subjects. Physiological measures including electrocardiagram (ECG) and galvanic skin response (GSR) were recorded throughout the whole experiment. The heart rate, the heart rate variability, and the event-related heart rate changes were calculated from the acquired ECG data. The frequency response of the GSR signal was calculated with a wavelet analysis. The study shows that the heart rate and heart rate variability parameters vary significantly between the training and experimental phase. GSR parameters and event-related heart rate changes show the occurrence of breaks in presence. Event-related heart rate changes also signified the virtual character utterances. There were also differences in response between participants who report more or less socially anxious.

Presence research relies heavily on empirical experiments involving subjects in mediated environments. Since presence is a complex, multidimensional concept, experiments on presence can be extremely resource intensive and produce large amounts of data of different types. As the presence community matures, we would like to suggest that data collected in experiments be made publicly available to the community. This will allow the verification of experimental results, comparison of results of experiments carried out in different laboratories, and evaluation of new data-analysis methods. This will, eventually, lead to consistency in approaches and increased confidence in results. In this paper we present the complete dataset from a large-scale experiment that we have carried out in highly immersive virtual reality. We describe the data we have gathered and give examples of the types of analysis that can be made based on that data.