Doctoral Researcher · University of Rome Tor Vergata
BACKGROUND
Biography
Stefano Bargione is a doctoral researcher studying brain function and behaviour using computational neuroscience and machine learning. His work includes EEG classification for brain–computer interfaces and the reconstruction of visual information from neural recordings. He holds a bachelor's degree in Psychological Sciences and Techniques from LUMSA University and a master's degree in Applied Experimental Psychological Sciences from the University of Milano-Bicocca. His research also examines human responses to multisensory and virtual environments.
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 engaged in a social interaction. To gain a more comprehensive understanding of social behavior, neuroscience research has increasingly adopted the hyperscanning technique that allows the simultaneous recording of neural activity in two or more interacting individuals. By considering interacting participants as a unique system, hyperscanning provides a deeper insight into the dynamic and reciprocal nature of social interactions, offering a more accurate representation of social behavior, including empathy. In the present study, we investigated empathy (i.e., the emotional responses elicited by positive and negative events) modulated by familiarity (friends or strangers) in two participants engaged in a computer-based game, while EEG data were simultaneously collected in both participants. EEG-hyperscanning data were analyzed using spectral analysis in the source domain and the results showed that both participants exhibited overlapping activations in brain regions related to the processing of negative events (insula). In addition, the agent observing the game manifested activations in areas associated with social cognition and typically involved during the evaluation of emotional and psychological states of others such as the Medial Prefrontal Cortex and the temporal lobes.Clinical RelevanceThe present study can help us to better understand the underlying social deficits that characterize psychopathological conditions by providing insights into the neurocognitive process of empathy and social evaluations systems.
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 participants for the ImageNet dataset and 10 for the THINGS-EEG 2 dataset, exposed to images spanning unique semantic categories. These EEG readings were converted into spectrograms, which were then used to train a convolutional neural network (CNN), integrated with a knowledge distillation procedure based on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image classification teacher network. This strategy allowed our model to attain a top-5 accuracy of 87%, significantly outperforming a standard CNN and various RNN-based benchmarks. Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images that had elicited EEG activity. Therefore, our architecture not only decodes images from neural activity but also offers a credible image reconstruction from EEG only, paving the way for, e.g., swift, individualized feedback experiments.