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Dive into the research topics where Alberto Betella is active.

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Featured researches published by Alberto Betella.


Frontiers in Neuroinformatics | 2015

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

Xerxes D. Arsiwalla; Riccardo Zucca; Alberto Betella; Enrique Martinez; David Dalmazzo; Pedro Omedas; Gustavo Deco; Paul F. M. J. Verschure

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.


Frontiers in Neuroscience | 2014

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

Alberto Betella; Riccardo Zucca; Ryszard Cetnarski; Alberto Greco; Antonio Lanata; Daniele Mazzei; Alessandro Tognetti; Xerxes D. Arsiwalla; Pedro Omedas; Danilo De Rossi; Paul F. M. J. Verschure

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.


PLOS ONE | 2016

The Affective Slider: A Digital Self-Assessment Scale for the Measurement of Human Emotions

Alberto Betella; Paul F. M. J. Verschure

Self-assessment methods are broadly employed in emotion research for the collection of subjective affective ratings. The Self-Assessment Manikin (SAM), a pictorial scale developed in the eighties for the measurement of pleasure, arousal, and dominance, is still among the most popular self-reporting tools, despite having been conceived upon design principles which are today obsolete. By leveraging on state-of-the-art user interfaces and metacommunicative pictorial representations, we developed the Affective Slider (AS), a digital self-reporting tool composed of two slider controls for the quick assessment of pleasure and arousal. To empirically validate the AS, we conducted a systematic comparison between AS and SAM in a task involving the emotional assessment of a series of images taken from the International Affective Picture System (IAPS), a database composed of pictures representing a wide range of semantic categories often used as a benchmark in psychological studies. Our results show that the AS is equivalent to SAM in the self-assessment of pleasure and arousal, with two added advantages: the AS does not require written instructions and it can be easily reproduced in latest-generation digital devices, including smartphones and tablets. Moreover, we compared new and normative IAPS ratings and found a general drop in reported arousal of pictorial stimuli. Not only do our results demonstrate that legacy scales for the self-report of affect can be replaced with new measurement tools developed in accordance to modern design principles, but also that standardized sets of stimuli which are widely adopted in research on human emotion are not as effective as they were in the past due to a general desensitization towards highly arousing content.


Journal of Experimental Psychology: Human Perception and Performance | 2013

The Social Perceptual Salience Effect

Martin Inderbitzin; Alberto Betella; Antonio Lanata; Enzo Pasquale Scilingo; Ulysses Bernardet; Paul F. M. J. Verschure

Affective processes appraise the salience of external stimuli preparing the agent for action. So far, the relationship between stimuli, affect, and action has been mainly studied in highly controlled laboratory conditions. In order to find the generalization of this relationship to social interaction, we assess the influence of the salience of social stimuli on human interaction. We constructed reality ball game in a mixed reality space where pairs of people collaborated in order to compete with an opposing team. We coupled the players with team members with varying social salience by using both physical and virtual representations of remote players (i.e., avatars). We observe that, irrespective of the team composition, winners and losers display significantly different inter- and intrateam spatial behaviors. We show that subjects regulate their interpersonal distance to both virtual and physical team members in similar ways, but in proportion to the vividness of the stimulus. As an independent validation of this social salience effect, we show that this behavioral effect is also displayed in physiological correlates of arousal. In addition, we found a strong correlation between performance, physiology, and the subjective reports of the subjects. Our results show that proxemics is consistent with affective responses, confirming the existence of a social salience effect. This provides further support for the so-called law of apparent reality, and it generalizes it to the social realm, where it can be used to design more efficient social artifacts.


virtual reality international conference | 2012

Embodied interaction with complex neuronal data in mixed-reality

Alberto Betella; Rodrigo Carvalho; Jesus Sanchez-Palencia; Ulysses Bernardet; Paul F. M. J. Verschure

The study of natural and artificial phenomena generates massive amounts of data in many areas of research. This data is frequently left unused due to the lack of tools to effectively extract, analyze and understand it. Visual representation techniques can play a key role in helping to discover patterns and meaning within this data. Neuroscience is one of the scientific fields that generates the most extensive datasets. For this reason we built a 3D real-time visualization system to graphically represent the massive connectivity of neuronal network models in the eXperience Induction Machine (XIM). The XIM is an immersive space equipped with a number of sensors and effectors that we constructed to conduct experiments in mixed-reality. Using this infrastructure we developed an embodied interaction framework that allows the user to move freely in the space and navigate through the neuronal system. We conducted an empirical evaluation of the impact of different navigation mappings on the understanding of a neuronal dataset. Our results revealed that different navigation mappings affect the structural understanding of the system and the involvement with the data presented.


Procedia Computer Science | 2015

Connectomics to Semantomics: Addressing the Brain's Big Data Challenge1☆

Xerxes D. Arsiwalla; David Dalmazzo; Riccardo Zucca; Alberto Betella; Santiago Brandi; Enrique Martinez; Pedro Omedas; Paul F. M. J. Verschure

Abstract Can semantic corpora be coupled to dynamical simulations in such a way so as to extract new associations from the data that were hitherto unapparent? We attempt to do this within neuroscience as an application domain, by introducing the notion of the semantome and coupling it to the connectome of the human brain network. This is implemented using BrainX3, a virtual reality simulation cum data mining platform that can be used for visualization, analysis and feature extraction of neuroscience data. We use this system to explore anatomical, functional and symptomatic semantics associated to simulated neuronal activity of a healthy brain, one with stroke and one perturbed by transcranial magnetic stimulation. In particular, we find that parietal and occipital lesions in stroke affect the visual processing pathway leading to symptoms such as visual neglect, depression and photo-sensitivity seizures. Integrating semantomics with connectomics thus generates hypotheses about symptoms, functions and brain activity that supplement existing tools for diagnosis of mental illness. Our results suggest a new approach to big data with potential applications to other domains.


virtual reality international conference | 2014

XIM-engine: a software framework to support the development of interactive applications that uses conscious and unconscious reactions in immersive mixed reality

Pedro Omedas; Alberto Betella; Riccardo Zucca; Xerxes D. Arsiwalla; Daniel Pacheco; Johannes Wagner; Florian Lingenfelser; Elisabeth André; Daniele Mazzei; Antonio Lanata; Alessandro Tognetti; Danilo De Rossi; Antoni Grau; Alex Goldhoorn; Edmundo Guerra; René Alquézar; Alberto Sanfeliu; Paul F. M. J. Verschure

The development of systems that allow multimodal interpretation of human-machine interaction is crucial to advance our understanding and validation of theoretical models of user behavior. In particular, a system capable of collecting, perceiving and interpreting unconscious behavior can provide rich contextual information for an interactive system. One possible application for such a system is in the exploration of complex data through immersion, where massive amounts of data are generated every day both by humans and computer processes that digitize information at different scales and resolutions thus exceeding our processing capacity. We need tools that accelerate our understanding and generation of hypotheses over the datasets, guide our searches and prevent data overload. We describe XIM-engine, a bio-inspired software framework designed to capture and analyze multi-modal human behavior in an immersive environment. The framework allows performing studies that can advance our understanding on the use of conscious and unconscious reactions in interactive systems.


international conference on computer graphics and interactive techniques | 2013

Advanced interfaces to stem the data deluge in mixed reality: placing human (un)consciousness in the loop

Alberto Betella; Enrique Martinez; Riccardo Zucca; Xerxes D. Arsiwalla; Pedro Omedas; Sytse Wierenga; Anna Mura; Johannes Wagner; Florian Lingenfelser; Elisabeth André; Daniele Mazzei; Alessandro Tognetti; Antonio Lanata; Danilo De Rossi; Paul F. M. J. Verschure

We live in an era of data deluge and this requires novel tools to effectively extract, analyze and understand the massive amounts of data produced by the study of natural and artificial phenomena in many areas of research.


virtual reality international conference | 2014

BrainX 3 : embodied exploration of neural data

Alberto Betella; Ryszard Cetnarski; Riccardo Zucca; Xerxes D. Arsiwalla; Enrique Martinez; Pedro Omedas; Anna Mura; Paul F. M. J. Verschure

We present BrainX3 as a novel immersive and interactive technology for exploration of large biological data, which in this paper is customized towards brain networks. Unlike traditional machine-inference systems, BrainX3 posits a two-way coupling of human intuition to powerful machine computation to tackle the big data challenge. Furthermore, through unobtrusive wearable sensors, BrainX3 can infer users states in terms of arousal and cognitive workload, thus changing the visualization and the sonification parameters to boost the exploration process.


BMC Neuroscience | 2013

The dynamic connectome: towards large-scale 3D reconstruction of brain activity in real-time

Xerxes D. Arsiwalla; Alberto Betella; Enrique Martinez; Pedro Omedas; Riccardo Zucca; Paul F. M. J. Verschure

What does large-scale connectivity tell us about whole-brain activity and neural circuits? In this work, we present a virtual reality based large-scale dynamic simulation for 3D reconstruction of whole-brain activity over the cortical connectome in real-time. Using DTI structural connectivity data from [1] we built an interactive 3D visualization of the human connectome network in an immersive virtual reality environment (Figure ​(Figure1A)1A) using the Unity 3D gaming engine. Further, the virtual reality brain network in Unity is coupled to a real-time neuronal simulator, iqr [2]. As we see, coupling structural connectivity data with detailed enough neuronal population dynamics is sufficient in predicting functional correlations and large-scale activity patterns. We model neuronal dynamics by a linear-threshold filter (as work in progress, we are currently implementing population dynamics from mean-field models [3]). Each population module is stochastic, having Gaussian noise. The user can stimulate any region or simultaneous regions of the network with external input currents. The simulation then reconstructs reverberating neural activity propagating throughout the network in real-time. As an explicit example, we stimulate the superior parietal areas and observe causal activity propagation in the parietal lobe, indicative of visuo-motor integration (Figure ​(Figure1B).1B). This is a first step to simulating and mapping large-scale brain activity in real-time. Figure 1 (A) snapshot of the cortical connectome network in virtual reality. (B) activation in the parietal lobes just after stimulation of the superior parietal area. Activity persists for about 5 secs after stimulation. Results and conclusions As quantitative analysis methods and data-recording technology in neuroscience make improvements, it is becoming evident large-scale dynamics and whole-brain quantitative measures play an important role. For instance, oscillations across large brain regions are precursors to several cognitive functions. Moreover, the causal map in these interactions is crucial. Compared to functional correlations, large-scale temporal activity maps across directionally connected brain structures serve as a more powerful tool to unravel mechanisms of large-scale neural circuits. Our results show that stimulating brain areas triggers a sequence of causal activations in associated network loops that represent cognitively related functions.

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Pedro Omedas

Pompeu Fabra University

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