Emotion recognition has acquired growing interest in many scientific and applied fields, proving to be crucial for the effectiveness of the human-machine interaction, the development of virtual reality environments, and the improvement of various clinical applications, such as supporting therapies for emotional disorders or assessing stress in work or educational contexts. One of the most widespread and reliable technologies for identifying mental and emotional states in a non-invasive way is the use of electroencephalographic (EEG) signals, which allow monitoring brain activity in real time and with high precision. Starting from EEG signals, it is possible to calculate specific indicators, such as Valence, Arousal, Dominance, Engagement and Stress, which allow to identify a person's emotional state with greater precision.