EEG-Music Emotion Recognition: Challenge Overview

Salvatore Calcagno, Simone Carnemolla, Isaak Kavasidis, Simone Palazzo, Daniela Giordano, Concetto Spampinato

Abstract: As our understanding of emotions continues to evolve, the ability of machines to accurately interpret and respond to emotional cues is more important than ever. Traditional methods of emotion recognition often fall short, particularly when it comes to the subtle and complex responses elicited by music. The EEG-Music Emotion Recognition Challenge aims to leverage electroencephalography (EEG) to decode emotional states from brain signals while subjects listen to music. This initiative seeks to uncover the intricate relationship between neural activity and emotional responses, offering insights for advancing adaptive user interfaces. We propose two tracks: (1) Person Identification aims to identify the subject from whom the EEG was recorded, while (2) Emotion Recognition targets the decoding of emotional state of the subject while listening to a musical stimulus.