Graph Neural Networks in EEG-based Emotion Recognition: A Survey
Abstract
Compared to other modalities, EEG-based emotion recognition can intuitively respond to the emotional patterns in the human brain and, therefore, has become one of the most concerning tasks in the brain-computer interfaces field. Since dependencies within brain regions are closely related to emotion, a significant trend is to develop Graph Neural Networks (GNNs) for EEG-based emotion recognition. However, brain region dependencies in emotional EEG have physiological bases that distinguish GNNs in this field from those in other time series fields. Besides, there is neither a comprehensive review nor guidance for constructing GNNs in EEG-based emotion recognition. In the survey, our categorization reveals the commonalities and differences of existing approaches under a unified framework of graph construction. We analyze and categorize methods from three stages in the framework to provide clear guidance on constructing GNNs in EEG-based emotion recognition. In addition, we discuss several open challenges and future directions, such as Temporal full-connected graph and Graph condensation.
Cite
@article{arxiv.2402.01138,
title = {Graph Neural Networks in EEG-based Emotion Recognition: A Survey},
author = {Chenyu Liu and Yuqiu Deng and Yihao Wu and Ruizhi Yang and Zhongruo Wang and Liangwei Zhang and Siyun Chen and Tianyi Zhang and Yang Liu and Yi Ding and Liming Zhai and Ziyu Jia and Xinliang Zhou},
journal= {arXiv preprint arXiv:2402.01138},
year = {2026}
}
Comments
The 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2026)