The activities of BRAVE Lab follow a coherent scientific and technological pathway.
We begin with the design of innovative experimental protocols, move to the acquisition and modelling of physiological signals, use these signals to infer emotional states, extend the analysis to interpersonal physiological coupling, and ultimately translate these models into adaptive systems and biofeedback technologies.
This integrated approach allows us not only to measure and understand human affective dynamics, but also to design systems that can positively influence social interaction, emotional regulation, and well-being.
Understanding and recognising human emotion objectively and continuously remains one of the major challenges. Despite advances in psychophysiology and artificial intelligence, we still lack reliable tools to measure how internal emotional states unfold over time and shape human experience.
At BRAVE Lab, we study emotion as a measurable physiological process. By combining wearable sensing, computational modelling, and adaptive technologies, we develop systems capable of estimating, reproducing, and modulating internal states directly from biosignals.
Building on this foundation, we extend our research to how emotional and physiological dynamics unfold during social interaction, investigating how individuals influence each other and how this knowledge can be translated into technologies that enhance well-being and human experience.
At BRAVE Lab, physiological signals are treated as rich, dynamic representations of the human internal state.
We work with multimodal recordings including ECG (from which heart rate variability is derived), EDA (electrodermal activity), respiration, and EEG, acquired through wearable and unobtrusive sensing systems. Our activity focuses on advanced preprocessing, feature extraction, and time-series modelling to transform raw biosignals into informative variables that reflect autonomic nervous system (ANS) activity. We develop statistical, machine learning, and Bayesian models capable of capturing the temporal structure and variability of physiological processes.
This foundational work enables the translation of biological dynamics into quantitative representations of latent states that can be used for emotion estimation, synchronization analysis, and real-time adaptive technologies.
BRAVE Lab develops computational AI models to infer emotional states from psychophysiological data.
Rather than relying solely on self-report or behavioral observation, we estimate continuous dimensions such as arousal, valence, stress, and anxiety using probabilistic, machine learning, and deep learning approaches.
Our research focuses on identifying the physiological signatures that underlie emotional dynamics and on building models that can operate also in real time. This enables the reconstruction of an individual’s internal emotional trajectory during interaction or exposure to complex environments.
From our perspective, affective computing represents the bridge between physiological signals and interpretable emotional representations that can inform adaptive systems, clinical tools, and human–AI interaction.
A central research theme at BRAVE Lab is the translation of computational models of physiology and emotion into adaptive technologies that interact with users in real time.
We develop biofeedback systems and virtual reality environments capable of adapting their behaviour according to the user’s estimated internal state. These systems are applied to scenarios such as social anxiety, phobias, therapeutic support, human–AI interaction, and social training.
By closing the loop between sensing, modelling, and actuation, we create environments that respond dynamically to the individual. This approach allows us not only to measure emotional processes, but also to influence and guide them through immersive, interactive technologies.
During social interaction, human physiological systems tend to align and influence each other in subtle but measurable ways.
At BRAVE Lab, we study this phenomenon as interpersonal physiological coupling, a process through which people affect each other emotionally and biologically. Using hyperscanning approaches and multimodal sensing, we analyse synchrony, directionality, and temporal alignment across heart rate variability, electrodermal activity, respiration, and neural signals. This work supports the idea that social connection can be described as a dynamical coupling between physiological processes.
Our models aim to quantify emotional contagion, bonding, and social coordination, providing an objective framework to study how humans connect beyond observable behaviour.
Forced Expiratory Volume in one second (FEV1) is a critical clinical marker used to assess the severity and progression of Chronic Obstructive Pulmonary Disease (COPD). Physical inactivity is an important predictor of mortality in patients with COPD and can be reliably measured over a few days through wearable sensors. To enable continuous respiratory monitoring outside...
Meditation is a complex cognitive practice associated with significant neurophysiological changes, particularly in long-term practitioners. These individuals represent an ideal human model for investigating neural changes associated to their consistent, frequent, and sustained cognitive engagement. However, in Western societies, long-term practitioners are relatively rare compared to Eastern monastic communities. In this study, we leverage a...
Difficulties in controlling emotions – a proxy for emotion dysregulation (ED)—and difficulties in expressing feelings in words—‘absence of emotion labelling’ or alexithymia—co-exist in autism and contribute to elevated levels of impulsive and suicidal behaviour. To date, studies linking the two phenomena have relied on retrospective self-reported measures, lacking support for generalizability to real-life situations. The...
Real-world gait speed assessment, has recently gained recognition as an important health indicator particularly in patients with chronic conditions such as Chronic Obstructive Pulmonary Disease (COPD). This study proposes an AI-based method for estimating gait speed in the real-world context in COPD patients using different combinations of three wearable devices: a smartphone, a smartwatch, and...
Meditation has been long associated with improvements in mental well-being, emotional regulation, and attentional control. Yet, the diversity of meditative techniques and participant expertise has hindered the systematic identification of their neurophysiological correlates supporting these benefits. To address this challenge, we investigated the neurophysiological signatures of concentrative and analytical meditation in 35 experienced Tibetan monk...
We will reply as soon as possible
Feel free to reach us for any need – whenever you want to (freely!) use the tools we developed, or you want us to share with you our data, or you want to interview us