News
Dataset Release
The dataset is now available at the EREMUS Kaggle page
Baseline implemenation
You can find baselines implementation in our GitHub repository.
Call for participation
As our understanding of emotions continues to evolve, the ability 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. This is where our challenge comes in.
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 detecting and treating affective disorders, as well as advancing adaptive user interfaces. We propose two tasks:
Task 1: Person Identification. Given a segment of EEG, identify the subject from whom the EEG was recorded.
Task 2: Emotion Recognition. Given a segment of EEG, classify the emotional state of the subject while listening to the musical stimulus. Labels are given in a discrete Valence-Dominance space.
We warmly welcome researchers from both academia and industry to participate in this challenging initiative. By joining us, you’ll contribute to a deeper understanding of how music influences emotions and how these responses can be decoded through EEG. Additionally, this challenge opens the potential for innovative applications in adaptive user interfaces and mental health monitoring.
The top 5 submissions will be invited to present their work at ICASSP 2025, with accepted papers included in the ICASSP proceedings. This special session will feature presentations from the top participants, followed by a panel discussion. Join us for this exciting event!
Intellectual property. The intellectual property (IP) is not transferred to the challenge organisers, i.e., if code is shared/submitted, the participants remain the owners of their code. When the code is made publicly available, an appropriate license should be added.