Stefano Bargione

PhD Student

s.bargione@campus.unimib.it

Biography

Stefano Bargione is a Ph.D. student in the National Ph.D. in AI, Health and Life Sciences track.

He has a BSc in Psychological Sciences and Techniques from LUMSA University and an M.Sc. in Applied Experimental Psychological Sciences from the University of Milano-Bicocca.

Stefano has experience in research internships and aims to master AI techniques for designing computational brain and behavior models.

His research interests include computational modeling approaches and cutting-edge technologies (e.g., VR, AR, XR) for designing customized computer-simulated scenarios based on subject-specific responses to multisensory experiences.

Profiles

Pubmed

Scopus

Google Scholar

Last 5 articles (Scopus)

When losing hurt together: investigating the role of Empathy during a card game by EEG Hyperscanning; Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference; 1 July 2025; DOI: 10.1109/EMBC58623.2025.11253022

Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models; Computers in Biology and Medicine; August 2024; DOI: 10.1016/j.compbiomed.2024.108701

Linking Brain Signals to Visual Concepts: CLIP based knowledge transfer for EEG Decoding and visual stimuli reconstruction; 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare Medicine and Biology Ieeeconf 2023; 2023; DOI: 10.1109/IEEECONF58974.2023.10404307

Last 5 articles (PubMed)

When losing hurt together: investigating the role of Empathy during a card game by EEG Hyperscanning
on 3 December 2025
Social neuroscience research investigates the neural basis of social cognition, which encompasses various cognitive abilities involved in processing social information, such as empathy. Empathy is a key function driving human socialization and can be defined as the ability to perceive others' feelings. When studying the neural basis of this process, recent research has highlighted the limitations of the traditional approach, which focuses on the study of the brain activity of a single subject...
Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models
on 20 June 2024
Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs knowledge distillation to train an EEG classifier and reconstruct images from the ImageNet and THINGS-EEG 2 datasets using only electroencephalography (EEG) data from participants who have viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6...