The USTC System for EEG-Music Emotion Recognition Challenge
Jiaxin Chen, Yiming Wang, Yin-Long Liu, Rui Feng, Jiahong Yuan, Zhen-Hua Ling
Abstract
This paper presents the Neural Harmony team’s submission to Task 1 (Person Identification) of the ICASSP 2025 EEG-Music Emotion Recognition Challenge, which aims to identify the subject from a given EEG segment. To enhance performance, we propose a novel architecture incorporating the Multiscale ConvBlock and integrating attention mechanisms with convolutional networks. We also reprocessed the data and trained multiple models with different train-validation splits, which were ensembled during testing to further improve robustness. Our final results on the test data exceed the challenge baseline, achieving 100% accuracy in Person Identification. Additionally, unseen subjects were introduced to evaluate the model’s generalization ability, and the results confirm the model’s strong adaptability to new subjects.
BibTeX
@inproceedings{icassp2025_theustcsystemfor,
title = {The USTC System for EEG-Music Emotion Recognition Challenge},
author = {Jiaxin Chen and Yiming Wang and Yin-Long Liu and Rui Feng and Jiahong Yuan and Zhen-Hua Ling},
booktitle = {ICASSP 2025},
year = {2025}
}