This paper explores the influence of passive haptic feedback on presence and task performance using two important interaction metaphors. We compared direct interaction with the user's hand with interaction using a stylus. Twenty-four participants performed a simple selection task consisting of pressing buttons while playing a memory game, with haptic feedback and interaction metaphor as the independent variables. We measured task performance by computing errors and time between button presses. We measured presence with questionnaires and through a new method based on users' involuntary movements. Our results suggest that passive haptic feedback improves both presence and task performance. However, small but significant differences related to the interaction metaphor were only apparent when haptic feedback was not provided.

An open question in research nowadays is the usability of brain–computer interfaces (BCI) conceived to extend human capabilities of interaction within a virtual environment. Several paradigms are used for BCI, but the steady-state visual-evoked potential (SSVEP) stands out as it provides a higher information transfer rate while requiring less training. It is an electroencephalographic response detectable when the user looks at a flickering visual stimulus. This research proposes a novel approach for SSVEP-based BCI controller used here for navigation within a 3D virtual environment. For the first time, the flickering stimuli were integrated into virtual objects as a part of the virtual scene in a more transparent and ecological way. As an example, when navigating inside a virtual natural outdoor scene, we could embed the SSVEP flashes in the wings of virtual butterflies surrounding the user. We could also introduce the use of animated and moving stimulations when using SSVEP-based BCI, as the virtual butterflies were left with the possibility of moving and flying in front of the user. Moreover, users received real-time feedback of their mental activity and were thus aware of their detected SSVEP directly and continuously. An experiment has been conducted to assess the influence of both the feedback and the integrated controller on navigation performance and subjective preference. We found that the usage of a controller integrated within the virtual scene along with the feedback seems to improve subjective preference and feeling of presence, despite reduced performance in terms of speed. This suggests that SSVEP-based BCI interfaces for virtual environments could move on from static targets and use integrated and animated stimuli presented in an ecological way for controls in systems where performance demands could be relaxed to benefit an improvement in interaction naturalness.

One of the main issues within the field of social robotics is to endow robots with the ability to direct attention to people with whom they are interacting. Different approaches follow bio-inspired mechanisms, merging audio and visual cues to localize a person using multiple sensors. However, most of these fusion mechanisms have been used in fixed systems, such as those used in video-conference rooms, and thus, they may incur difficulties when constrained to the sensors with which a robot can be equipped. Besides, within the scope of interactive autonomous robots, there is a lack in terms of evaluating the benefits of audio-visual attention mechanisms, compared to only audio or visual approaches, in real scenarios. Most of the tests conducted have been within controlled environments, at short distances and/or with off-line performance measurements. With the goal of demonstrating the benefit of fusing sensory information with a Bayes inference for interactive robotics, this paper presents a system for localizing a person by processing visual and audio data. Moreover, the performance of this system is evaluated and compared via considering the technical limitations of unimodal systems. The experiments show the promise of the proposed approach for the proactive detection and tracking of speakers in a human-robot interactive framework.

Brain-computer interfaces (BCIs) are becaming more available to the general public, and have already been used to control applications such as computer games. One disadvantage is that they are not completely reliable. In order to increase BCI performances, some low-level adjustments can be made, such as signal processing, as well as high level adjustments such as modifying the controller paradigm. In this study, we explore a novel, context-dependant, approach for a steady-state visual-evoked potential (SSVEP)-based BCI controller. This controller uses two kinds of behavior alternation: commands can be added and removed if their use is irrelevant to the context and the actions resulting from their activation can be weighted depending on the likeliness of the actual intention of the user. This controller has been integrated within a BCI computer game and its influence in performance and mental workload has been addressed through a pilot experiment. Preliminary results have shown a workload reduction and performance improvement with the context-dependent controller, while keeping the engagement levels untouched.

In this paper we explore the use of electrical biosignals measured on scalp and corresponding to mental relaxation and concentration tasks in order to control an object in a video game. To evaluate the requirements of such a system in terms of sensors and signal processing we compare two designs. The first one uses only one scalp electroencephalographic (EEG) electrode and the power in the alpha frequency band. The second one uses sixteen scalp EEG electrodes and machine-learning methods. The role of muscular activity is also evaluated using five electrodes positioned on the face and the neck. Results show that the first design enabled 70% of the participants to successfully control the game, whereas 100% of the participants managed to do it with the second design based on machine learning. Subjective questionnaires confirm these results: users globally felt to have control in both designs, with an increased feeling of control in the second one. Offline analysis of face and neck muscle activity shows that this activity could also be used to distinguish between relaxation and concentration tasks. Results suggest that the combination of muscular and brain activity could improve performance of this kind of system. They also suggest that muscular activity has probably been recorded by EEG electrodes.