This paper reviews the move from virtual reality exposure-based therapy (VRET) to augmented reality exposure-based therapy (ARET). Unlike virtual reality (VR), which entails a complete virtual environment (VE), augmented reality (AR) limits itself to producing certain virtual elements to then merge them into the view of the physical world. Although the general public may only have become aware of AR in the last few years, AR type applications have been around since beginning of the 20th century. Since, then, technological developments have enabled an ever increasing level of seamless integration of virtual and physical elements into one view. Like VR, AR allows the exposure to stimuli which, due to various reasons, may not be suitable for real-life scenarios. As such, AR has proven itself to be a medium through which individuals suffering from specific phobia can be exposed “safely” to the object(s) of their fear, without the costs associated with programming complete virtual environments. Thus, ARET can offer an efficacious alternative to some less advantageous exposure-based therapies. Above and beyond presenting what has been accomplished in ARET, this paper also raises some ARET related issues, and proposes potential avenues to be followed. These include the definition of an AR related term, the type of measures to be used to qualify the user’s experience in an augmented reality environment (ARE), the development of alternative geospatial referencing systems, as well as the potential use of ARET to treat social phobia. Overall, it may be said that the use of ARET, although promising, is still in its infancy but that, given a continued cooperation between clinical and technical teams, ARET has the potential of going well beyond the treatment of small animal phobia.

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Moving from virtual reality exposure-based therapy to augmented reality exposure-based therapy: a review.

Musings on telepresence and virtual presence

This paper presents a systematic review of electroencephalography (EEG)-based brain---computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.

EEG-based BCI and video games: a progress report

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field.

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Electroencephalography (EEG)-Based Brain–Computer Interfaces

Moving around in the world is naturally a multisensory experience, but today's embodied agents are deaf---restricted to solely their visual perception of the environment. We introduce audio-visual navigation for complex, acoustically and visually realistic 3D environments. By both seeing and hearing, the agent must learn to navigate to a sounding object. We propose a multi-modal deep reinforcement learning approach to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets. We further introduce SoundSpaces: a first-of-its-kind dataset of audio renderings based on geometrical acoustic simulations for two sets of publicly available 3D environments (Matterport3D and Replica), and we instrument Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of real-world scanned environments. Our results show that audio greatly benefits embodied visual navigation in 3D spaces, and our work lays groundwork for new research in embodied AI with audio-visual perception.

SoundSpaces: Audio-Visual Navigation in 3D Environments.

Evaluation of generalized cross-correlation methods for direction of arrival estimation using two microphones in real environments

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.

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The influence of passive haptic feedback and difference interaction metaphors on presence and task performance

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.

https://www.mdpi.com/1424-8220/14/6/9522/pdf

Audio-Visual Perception System for a Humanoid Robotic Head

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.

Toward Contextual SSVEP-Based BCI Controller: Smart Activation of Stimuli and Control Weighting

In this chapter, an experimental study is presented for evaluating the importance of binocular disparity in depth perception within a Virtual Environment (VE), which is assumed to be critical in many manipulation tasks. In this research work, two assumptions are made: Size cues strongly contaminate depth perception mechanisms and binocular disparity optimizes depth perception for manipulation tasks in VE. The results outline size cues as possible cause of depth perception degradation and binocular disparity as an important factor in depth perception, whose influence is altered by the position within a VE.

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Raquel Viciana-Abad

Papers

Evaluation of generalized cross-correlation methods for direction of arrival estimation using two microphones in real environments

The influence of passive haptic feedback and difference interaction metaphors on presence and task performance

Audio-Visual Perception System for a Humanoid Robotic Head

Toward Contextual SSVEP-Based BCI Controller: Smart Activation of Stimuli and Control Weighting

Influence of Binocular Disparity in Depth Perception Mechanisms in Virtual Environments