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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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...

Emotional Responses to AI Use: Development of the SAG-AI Scale for Shame and Guilt

E Cipriani, D Menicucci, A Greco, S Grassini International Journal of Human–Computer Interaction, 1-20, 2026

The rise of generative AI tools has introduced new ethical dilemmas and emotional responses, yet little research has examined users’ social emotions such as shame, guilt, and impostor syndrome. This article presents the SAG-AI (Shame and Guilt related to AI tools) scale, a 9-item psychometric instrument developed to measure these emotions in AI tool usage....

FEV1 Prediction from Sensor Data of a Physical Activity Armband in COPD Patients: Results from the COSYCONET Cohort

F Bossi, M Laurino, N Carbonaro, B Waschki, H Watz, M Abdo, A Tognetti, ... IEEE Access, 2026

Forced Expiratory Volume in one second (FEV ) 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...

Dynamic causal modeling of low-density resting-state EEG in long-term meditation practitioners

G Rho, F Bossi, N Norbu, J Kechok, N Sherab, J Soepa, J Thakchoe, ... Scientific Reports, 2026

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...

Exploring emotion control and alexithymia in autistic adults: An ecological momentary assessment study

ME Costache, F Gioia, N Vanello, A Greco, F Lefebvre, A Capobianco, ... Journal of Autism and Developmental Disorders 56 (2), 587-601, 2026

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...

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