Affective Computing is a field of research that "relates to, arises from, or deliberately influences emotion or other affective phenomena" (Picard, MIT Press 1997).

Many recent theories of emotion have shown the autonomic nervous system (ANS) activity as one of the major component of the emotion response. Indeed, changes in emotional states often reflect modifications in facial expressions, voice, gestures and other autonomic correlates (e.g., electrodermal activity (EDA), heart rate variability (HRV)), in order to communicate, sometimes unconsciously, personal feelings to other people.

The automatic emotion recognition is one of the most important applications in neuroscience and, often, is identified within the so-called affective computing field. Mainly, such a technical field refers to the engineering approaches able to link physiological patterns to different emotions.

Our research focuses mainly on the development of new mathematical methods, models and devices in order to advance prior understanding of emotional processes. Particularly, we design new technology to acquire ANS signals such as HRV, EDA, respiration, eye-gaze, and we developed novel mathematical methods to estimate possible biomarkers able to characterize the emotional state of a subject and its possible dysregulation.

For example, over the last two decades, we have demonstrated that heart rate variability linear and nonlinear analysis may provide effective measures of regulated emotional response. We also contributed by revealing real-time emotional response estimated using time-varying heartbeat linear and nonlinear dynamics only.

Among several studies, a special mention goes to the inhomogeneous point-process framework for heartbeat dynamics to perform instantaneous emotion recognition.



Virtual reality applications are currently under extensive investigation in our labs.

Brain correlates of emotions have also been extensively studied, along with related functional brain-heart interplay.

J Marin-Morales, JL Higuera-Trujillo, J Guixeres, C Llinares, M Alcañiz, and G Valenza, "Heart rate variability analysis for the assessment of immersive emotional arousal using virtual reality: Comparing real and virtual scenarios". Plos One, vol 17, num 7, e0254098, 2021.

J Marín-Morales, J L Higuera-Trujillo, A Greco, J Guixeres, C Llinares, EP Scilingo, M Alcañiz, and G Valenza, "Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors". Scientific Reports, vol. 8, num. 13657, 2018.

G. Valenza, L. Citi, A. Lanata, E.P. Scilingo, and R. Barbieri, "Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics, Scientific Reports". vol. 4, 4998, pp. 1-13, 2014