Cortical processing of emotional faces via EEG

Simulation theory proposes that when an individual observes emotional facial expressions, there is an automatic activation of neural circuits. This neuronal activation allows individuals to mentally reproduce the emotion and, consequently, to recognize it. In other words, an internal mirroring process is generated within the individual, facilitating the recognition of others’ emotions and the understanding of their affective states. In this theoretical framework, the ability to recognize emotions on others’ faces, commonly known as Facial Emotion Recognition (FER), represents a fundamental aspect of social sphere, supporting empathy and facilitating interaction and communication. In this context, the mu rhythm plays a pivotal role; it is a sensorimotor oscillation considered an indicator of Mirror Neuron System (MNS) activity during facial emotion recognition. Previous studies have reported mu rhythm suppression during the observation of biological actions and emotional videos. These studies have shown that there is a link between the activity of the mirror neuron system and emotional understanding. However, it has not yet been fully explored whether static facial expressions are sufficient to elicit such suppression. The aim of this thesis is to investigate the role of the EEG mu rhythm. To understand whether static stimuli elicit activation of the MNS, EEG data were collected while participants viewed 350 static emotional faces taken from the Bosphorus Database. After viewing each image, each participant answered a question indicating which emotion they believed was being expressed. Once the recordings were obtained, the signals underwent preprocessing, and changes in the mu rhythm were quantified through Event-Related Desynchronization (ERD), expressed as the percentage variation in power between the baseline and active viewing conditions. The study aims to determine whether such stimuli elicit significant mu suppression and to explore potential differences in its magnitude; additionally, suppression is compared between negative and positive emotions. Furthermore, it investigates whether participants’ response times correlate with the timing of ERD onset by analysing both early and late ERD components, to characterize the temporal dynamics of cortical activity underlying facial emotion processing. These findings are expected to clarify whether static emotional stimuli activate the mirror neuron system similarly to dynamic ones, helping to gain greater clarity of the neural mechanisms.