Interpreting Psychophysiological States Using Unobtrusive Wearable Sensors in Virtual Reality

One of the main challenges in the study of human be- havior is to quantitatively assess the participants’ affective states by measuring their psychophysiological signals in ecologically valid conditions. The quality of the acquired data, in fact, is often poor due to artifacts generated by natural interactions such as full body movements and gestures. We created a technology to address this problem. We enhanced the eXperience Induction Machine (XIM), an immersive space we built to conduct experiments on human behavior, with unobtrusive wearable sensors that measure electrocardiogram, breathing rate and electrodermal response. We conducted an empirical validation where participants wearing these sensors were free to move in the XIM space while exposed to a series of visual stimuli taken from the International Affective Picture System (IAPS). Our main result consists in the quan- titative estimation of the arousal range of the affective stimuli through the analysis of participants’ psychophysiological states. Taken together, our findings show that the XIM constitutes a novel tool to study human behavior in life-like conditions

Manipulating complex network structures in virtual reality and 3D printing of the results

Comunicació presentada a: 2014 Virtual Reality International Conference (VRIC 2014), celebrada a Laval, França, del 9 a l'11 d'abril de 2014We present an immersive VR system that allows to manipulate network data structures by creating, removing or reconfiguring their elements and export the results at any time for direct 3D printing.EU FP7-ICT-2009-5 grant agreement n. 258749 [CEEDS]. The Generalitat de Catalunya (CUR, DIUE) and the European Social Fund are supporting this research

Spatializing experience: a framework for the geolocalization, visualization and exploration of historical data using VR/AR technologies

Comunicació presentada a: the 2014 Virtual Reality International Conference (VRIC 2014), celebrada a Laval, França, del 9 al'11 d'abril de 2014In this study we present a novel ICT framework for the exploration and visualization of historical information using Augmented Reality (AR) and geolocalization. The framework facilitates the geolocalization of multimedia les, as well as their later retrieval and visualization through an AR paradigm in which a virtual reconstruction is matched to user's positions and viewing angle. The main objective of the architecture is to enhance human-data interaction with cultural heritage content in outdoor settings and generate more engaging and profound learning experiences by exploiting information spatialization and sequencing strategies.This research received funding from the European Community's Seventh Framework Programme (FP7-ICT-2009-5) under grant agreement n. 258749 [CEEDS]

Spatializing experience: a framework for the geolocalization, visualization and exploration of historical data using VR/AR technologies

Comunicació presentada a: the 2014 Virtual Reality International Conference (VRIC 2014), celebrada a Laval, França, del 9 al'11 d'abril de 2014In this study we present a novel ICT framework for the exploration and visualization of historical information using Augmented Reality (AR) and geolocalization. The framework facilitates the geolocalization of multimedia les, as well as their later retrieval and visualization through an AR paradigm in which a virtual reconstruction is matched to user's positions and viewing angle. The main objective of the architecture is to enhance human-data interaction with cultural heritage content in outdoor settings and generate more engaging and profound learning experiences by exploiting information spatialization and sequencing strategies.This research received funding from the European Community's Seventh Framework Programme (FP7-ICT-2009-5) under grant agreement n. 258749 [CEEDS]

Understanding large network datasets through embodied interaction in virtual reality

Comunicació presentada a: the 2014 Virtual Reality International Conference (VRIC 2014), celebrada del 9 a l'11 d'abril de 2014 a Laval, FrançaThe quantity of information we are producing is soaring: this generates large amounts of data that are frequently left unexplored. Novel tools are needed to stem this “data deluge”. We developed a system that enhances the understanding of large datasets through embodied navigation and natural gestures using the immersive mixed reality space called “eXperience Induction Machine” (XIM). One of the applications of our system is in the exploration of the human brain connectome: the network of nodes and connections that defines the information flow in the brain. We exposed participants to a connectome dataset using either our system or a state of the art software for visualization and analysis of connectomic data. We measured their understanding and visual memory of the connectome structure. Our results showed that participants retained more information about the structure of the network when using our system. Overall, our system constitutes a novel approach in the exploration and understanding of large network datasets.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7- ICT-2009-5) under grant agreement n. 258749 [CEEDS]. Thanks to the Comissionat per a Universitats i Recerca (CUR) del Departament d’Innovaci´o, Universitats i Empresa (DIUE) of the Generalitat de Catalunya and to the European Social Fund for supporting this research

Understanding large network datasets through embodied interaction in virtual reality

Comunicació presentada a: the 2014 Virtual Reality International Conference (VRIC 2014), celebrada del 9 a l'11 d'abril de 2014 a Laval, FrançaThe quantity of information we are producing is soaring: this generates large amounts of data that are frequently left unexplored. Novel tools are needed to stem this “data deluge”. We developed a system that enhances the understanding of large datasets through embodied navigation and natural gestures using the immersive mixed reality space called “eXperience Induction Machine” (XIM). One of the applications of our system is in the exploration of the human brain connectome: the network of nodes and connections that defines the information flow in the brain. We exposed participants to a connectome dataset using either our system or a state of the art software for visualization and analysis of connectomic data. We measured their understanding and visual memory of the connectome structure. Our results showed that participants retained more information about the structure of the network when using our system. Overall, our system constitutes a novel approach in the exploration and understanding of large network datasets.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7- ICT-2009-5) under grant agreement n. 258749 [CEEDS]. Thanks to the Comissionat per a Universitats i Recerca (CUR) del Departament d’Innovació, Universitats i Empresa (DIUE) of the Generalitat de Catalunya and to the European Social Fund for supporting this research

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.This research has been supported by the EC FP7 project, CEEDS (FP7-ICT-2009-5), under grant agreement n. 258749

A virtual reality system for the simulation of neurodiversity

Comunicació presentada a 6th International Congress on Information and Communication Technology (ICICT 2021), celebrat del 25 al 26 de febrer de 2021 de manera virtual.Autism is a neurodevelopmental disorder characterized by deficits in social communication and repetitive patterns of behavior. Individuals affected by Autism Spectrum Disorder (ASD) may face overwhelming sensory hypersensitivities that hamper their everyday life. In order to promote awareness about neurodiversity among the neurotypical population, we have developed an interactive virtual reality simulation to experience the oversensory stimulation that an individual with autism spectrum disorder may experience in a natural environment. In this experience, we project the user in a first-person perspective in a classroom where a teacher is presenting a lecture. As the user explores the classroom and attends the lecture, he/she is confronted with sensory distortions which are commonly experienced by persons with ASD. We provide the users with a virtual reality headset with motion tracking, two wireless controllers for interaction, and a wristband for physiological data acquisition to create a closed feedback loop. This wearable device measures blood volume pulse (BVP) and electrodermal activity (EDA), which we use to perform online estimations of the arousal levels of users as they respond to the virtual stimuli. We use this information to modulate the intensity of auditory and visual stimuli simulating a vicious cycle in which increased arousal translates into increased oversensory stimulation. Here, we present the architecture and technical implementation of this system.This research received funding from H2020-EU, ID: 787061 and ERC (PoC) H2020-EU, ID: 840052

Inference of human affective states from psychophysiological measurements extracted under ecologically valid conditions

Compared to standard laboratory protocols, the measurement of psychophysiological signals in real world experiments poses technical and methodological challenges due to external factors that cannot be directly controlled. To address this problem, we propose a hybrid approach based on an immersive and human accessible space called the eXperience Induction Machine (XIM), that incorporates the advantages of a laboratory within a life-like setting. The XIM integrates unobtrusive wearable sensors for the acquisition of psychophysiological signals suitable for ambulatory emotion research. In this paper, we present results from two different studies conducted to validate the XIM as a general-purpose sensing infrastructure for the study of human affective states under ecologically valid conditions. In the first investigation, we recorded and classified signals from subjects exposed to pictorial stimuli corresponding to a range of arousal levels, while they were free to walk and gesticulate. In the second study, we designed an experiment that follows the classical conditioning paradigm, a well-known procedure in the behavioral sciences, with the additional feature that participants were free to move in the physical space, as opposed to similar studies measuring physiological signals in constrained laboratory settings. Our results indicate that, by using our sensing infrastructure, it is indeed possible to infer human event-elicited affective states through measurements of psychophysiological signals under ecological conditions.The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7-ICT-2009-5) under grant agreement n. 258749 [CEEDS]. The Generalitat de Catalunya (CUR, DIUE) and the European Social Fund are supporting this research