EEG-Music Emotion Recognition: Challenge Overview

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.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10888506
Citations
3
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
4.29
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article

EEG-Music Emotion Recognition: Challenge Overview

Isaak Kavasidis, Simone Palazzo, Concetto Spampinato, Daniela Giordano et al.
3 citations
EEG and Brain-Computer Interfaces
4.29
article

EEG-Music Emotion Recognition: Challenge Overview

Isaak Kavasidis, Simone Palazzo, Concetto Spampinato, Daniela Giordano, Salvatore Calcagno, Simone Carnemolla
article en
3 citations

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.

University of Catania (IT)
Openalex Percentile: Top 7%
EEG and Brain-Computer Interfaces
4.29
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