This review aims to evaluate the effectiveness of virtual reality-based interventions (VR-based interventions) on cognitive deficits in children with attention deficit hyperactivity disorder (ADHD). A systematic review and meta-analysis were performed according to the PRISMA statement and the Cochrane Handbook guidelines for conducting meta-analyses. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was used to assess the quality of the evidence. Clinical trials published up to 29 October 2020, were included. The meta-analysis included four studies, with a population of 125 participants with ADHD. The magnitude of the effect was large for omissions (SMD = −1.38; p = 0.009), correct hits (SMD = −1.50; p = 0.004), and perceptual sensitivity (SMD = −1.07; p = 0.01); and moderate for commissions (SMD = −0.62; p = 0.002) and reaction time (SMD = −0.67; p = 0.03). The use of VR-based interventions for cognitive rehabilitation in children with ADHD is limited. The results showed that VR-based interventions are more effective in improving sustained attention. Improvements were observed in attentional vigilance measures, increasing the number of correct responses and decreasing the number of errors of omission. No improvements were observed in impulsivity responses.

The emergence of off-screen interaction devices is bringing the field of virtual reality to a broad range of applications where virtual objects can be manipulated without the use of traditional peripherals. However, to facilitate object interaction, other stimuli such as haptic feedback are necessary to improve the user experience. To enable the identification of virtual 3D objects without visual feedback, a haptic display based on a vibrotactile glove and multiple points of contact gives users an enhanced sensation of touching a virtual object with their hands. Experimental results demonstrate the capacity of this technology in practical applications.

Impairments in social cognition have been described in several psychiatric and neurological disorders. Given the importance of the relationship between social cognition and functioning and quality of life in these disorders, there is a growing interest in social cognition remediation interventions. The aim of this study was to carry out a systematic mapping review to describe the state of the art in social cognition training and remediation interventions. Publications from 2006 to 2016 on social cognition interventions were reviewed in four databases: Scopus, PsycINFO, PubMed and Embase. From the initial result set of 3229 publications, a final total of 241 publications were selected. The study revealed an increasing interest in social cognition remediation interventions, especially in the fields of psychiatry and psychology, with a gradual growth in the number of publications. These were frequently published in high impact factor journals and underpinned by robust scientific evidence. Most studies were conducted on schizophrenia, followed by autism spectrum disorders. Theory of mind and emotional processing were the focus of most interventions, whilst a limited number of studies addressed attributional bias and social perception. Targeted interventions in social cognition were the most frequent practice in the selected papers, followed by non-specific treatment interventions and broad-based interventions. Research in social cognition remediation interventions is growing. Further studies are needed on attributional bias and social perception remediation programs, while the comparative efficacy of different interventions also remains unclear.

Estimation of human emotions plays an important role in the development of modern brain-computer interface devices like the Emotiv EPOC+ headset. In this paper, we present an experiment to assess the classification accuracy of the emotional states provided by the headset’s application programming interface (API). In this experiment, several sets of images selected from the International Affective Picture System (IAPS) dataset are shown to sixteen participants wearing the headset. Firstly, the participants’ responses in form of a self-assessment manikin questionnaire to the emotions elicited are compared with the validated IAPS predefined valence, arousal and dominance values. After statistically demonstrating that the responses are highly correlated with the IAPS values, several artificial neural networks (ANNs) based on the multilayer perceptron architecture are tested to calculate the classification accuracy of the Emotiv EPOC+ API emotional outcomes. The best result is obtained for an ANN configuration with three hidden layers, and 30, 8 and 3 neurons for layers 1, 2 and 3, respectively. This configuration offers 85% classification accuracy, which means that the emotional estimation provided by the headset can be used with high confidence in real-time applications that are based on users’ emotional states. Thus the emotional states given by the headset’s API may be used with no further processing of the electroencephalogram signals acquired from the scalp, which would add a level of difficulty.

The use of haptics in virtual reality and video games has received growing attention as a means of enhancing the sensation of immersion in these environments. The sensation of touching virtual objects not only augments the impression of reality but also can improve the performance. However, the design of haptic interactions is not an easy task, and it usually needs a great effort due to the absence of powerful prototyping toolkits. Thus, this article proposes a vibrotactile prototyping toolkit for Eccentric Rotating Mass actuators named VITAKI. The main objective of this platform is to facilitate the prototyping and testing procedures of new vibrotactile interaction techniques for Virtual Reality and video games. A detailed description of the design of the system is provided, presenting the hardware and software elements that make up the VITAKI toolkit. In addition, its application to two different examples to illustrate its use is provided. Finally, a preliminary evaluation of this toolkit is presented. This evaluation is divided into two main stages. On one hand, a study of Olsen’s criteria is performed to analyze its general capabilities. On the other hand, a comparison with previously presented proposals is included too. These two analyses, together with other experiments where the devices created with the toolkit were tested by end users, highlight its main features and its advantages over other proposals.