Classifying Music-Induced Emotion Using Multi-Modal Ensembles of EEG and Audio Feature Models

In this paper, we present our submission to the EEG-Music Emotion Recognition Challenge at ICASSP 2025. Our work focused on Task 2, where the objective was to classify the emotional state of subjects in a discrete valence-arousal space while they listen to music. Our proposed solution adopts an ensemble approach, integrating electroencephalography (EEG) signals, raw audio signals, and song features. We incorporated a diverse set of models, including Audio Spectrogram Transformer (AST) [1] and a dedicated EEG model. By combining insights from these modalities, we aimed to capture the interplay between music and emotional responses. We achieved a balanced accuracy of 41.34% on held-out data, improving the baseline by more than 11% and thus 2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> place in the competition.