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.

We studied the impact of different visual objects such as a moving hand and a moving cube on the bioelectrical brain activity (i.e., electroencephalogram; EEG). The moving objects were presented in a virtual reality (VR) system via a head mounted display (HMD). Nine healthy volunteers were confronted with 3D visual stimulus presentations in four experimental conditions: (i) static hand, (ii) dynamic hand, (iii) static cube, and (iv) dynamic cube. The results reveal that the processing of moving visual stimuli depends on the type of object: viewing a moving hand results in a stronger desynchronization of the central beta rhythm than viewing a moving cube. This provides further evidence for some extent of motor processing related to visual presentation of objects and implies a greater involvement of motor areas in the brain with the observation of action of different body parts than with the observation of non-body part movements.

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.