Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications.

The human brain's role in face processing (FP) and decision making for social interactions depends on recognizing faces accurately. However, the prevalence of deepfakes, AI-generated images, poses challenges in discerning real from synthetic identities. This study investigated healthy individuals' cognitive and emotional engagement in a visual discrimination task involving real and deepfake human faces expressing positive, negative, or neutral emotions. Electroencephalographic (EEG) data were collected from 23 healthy participants using a 21-channel dry-EEG headset; power spectrum and event-related potential (ERP) analyses were performed. Results revealed statistically significant activations in specific brain areas depending on the authenticity and emotional content of the stimuli. Power spectrum analysis highlighted a right-hemisphere predominance in theta, alpha, high-beta, and gamma bands for real faces, while deepfakes mainly affected the frontal and occipital areas in the delta band. ERP analysis hinted at the possibility of discriminating between real and synthetic faces, as N250 (200-300 ms after stimulus onset) peak latency decreased when observing real faces in the right frontal (LF) and left temporo-occipital (LTO) areas, but also within emotions, as P100 (90-140 ms) peak amplitude was found higher in the right temporo-occipital (RTO) area for happy faces with respect to neutral and sad ones.

The capacity of action observation to drag the trainees' motor pattern toward the observed model.

Action Observation Training (AOT) promotes the acquisition of motor abilities. However, while the cortical modulations associated with the AOT efficacy are well known, few studies investigated the AOT peripheral neural correlates and whether their dynamics move towards the observed model during the training. We administered seventy-two participants (randomized into AOT and Control groups) with training for learning to grasp marbles with chopsticks. Execution practice was preceded by an observation session, in which AOT participants observed an expert performing the task, whereas controls observed landscape videos. Behavioral indices were measured, and three hand muscles' electromyographic (EMG) activity was recorded and compared with the expert. Behaviorally, both groups improved during the training, with AOT outperforming controls. The EMG trainee-model similarity also increased during the training, but only for the AOT group. When combining behavioral and EMG similarity findings, no global relationship emerged; however, behavioral improvements were "locally" predicted by the similarity gain in muscles and action phases more related to the specific motor act. These findings reveal that AOT plays a magnetic role in motor learning, attracting the trainee's motor pattern toward the observed model and paving the way for developing online monitoring tools and neurofeedback protocols.