BRAVE Lab’s PI, Alberto Greco

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.

Research question

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.

BRAVE Lab’s activity

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.

What we are building

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.

Our research pathway is articulated through four tightly connected research areas ↓

Physiological Signal Processing & Modelling

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.

Here is our most representative paper about this research line

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Affective Computing

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.

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Adaptive Systems, Virtual Reality and Biofeedback

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.

Here is our most representative paper about this research line

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Interpersonal Physiological Coupling

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.

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Greco Alberto

Research Center "E.Piaggio", School of Engineering, University of Pisa, Italy

Verified email at centropiaggio.unipi.it

Biomedical System and Signal Processing Physiological models Wearable monitoring systems Electrodermal activity

A Deep Learning approach for estimating Time-Domain Heart Rate Variability parameters from wrist photoplethysmography during daily activity

G Rho, N Carbonaro, M Laurino, A Tognetti, A Greco Biomedical Signal Processing and Control 123, 110531, 2026

AbstractHeart rate variability (HRV) is a critical indicator of autonomic nervous system regulation and cardiovascular health, typically measured using electrocardiography (ECG). Wrist devices are gaining popularity as non-invasive alternatives to monitor heart rate (HR) and pulse rate variability (PRV) in unconstrained setting through photoplethysmography (PPG). However, movement artifacts severely deteriorate signal quality, making estimation reliability...

Integrated central and autonomic signatures of concentrative and analytical meditation in expert practitioners

AL Callara, MH Azarabad, L Sebastiani, N Sherab, J Khechok, J Tsering, ... Scientific Reports, 2026

Meditation has long been associated with improvements in mental well-being, emotional regulation, and attentional control. However, the diversity of meditative techniques and variability in participant expertise across studies have hindered the systematic identification of their neurophysiological correlates. To address this challenge, we investigated the neurophysiological signatures of distinct meditation types in a relatively homogeneous cohort...

Interpersonal autonomic coupling profiles distinguish social transmission from common-input synchrony

M De Marinis, A Gargano, F Bossi, S Frumento, A Callara, E Scilingo, ...

Interpersonal physiological synchrony is often treated as an embodied marker of social connection, but it remains unclear when physiological alignment reflects interpersonal transmission rather than common input. Here, using a preregistered design, previously unacquainted dyads (N= 48, 24 dyads) completed a social Leader-Follower condition and a matched non-social co-exposure condition in which both participants viewed...

A Machine Learning Approach for Sleep Apnea Detection Using Unobtrusive In-Bed Sensors

R Marinsalda, E Melissa, C Marinai, L Arcarisi, F Bossi, P Bufano, ... 2026 IEEE International Conference on Smart Computing Workshops and Other …, 2026

Sleep apnea (SA) is a prevalent breathing disorder whose diagnosis still relies on polysomnography (PSG), motivating the development of unobtrusive in-bed sensing technologies as scalable alternatives. In this study, we present a machine learning algorithm based on temporal feature engineering to detect simulated SA events using our Smart Mattress Cover (SMC) system, which is the...

Multimodal Classification of Social Anxiety Using Continuous Self-Rating and Physiological Data in Virtual Reality

M Pardini, S Frumento, M Martini, M Alaimo, G Rho, N Paparo, ... IEEE Transactions on Affective Computing, 2026

Assessment of Social Anxiety Disorder (SAD) is limited by paradigms that often target performance-related fears and its reliance on static self-reports that fail to capture the dynamic, in-the-moment nature of anxiety. This study introduces a novel Virtual Reality (VR) framework to assess SAD that integrates continuous self-ratings of anxiety with objective physiological data. Sixty-three participants...

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