Graph Neural Networks in EEG-based Emotion Recognition: A Survey
<|reference_start|>Graph Neural Networks in EEG-based Emotion Recognition: A Survey: 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.<|reference_end|>
@article{liu2024graph,
    title={Graph Neural Networks in EEG-based Emotion Recognition: A Survey},
    author={Chenyu Liu, Xinliang Zhou, Yihao Wu, Ruizhi Yang, Zhongruo Wang,
  Liming Zhai, Ziyu Jia and Yang Liu},
    journal={arXiv preprint arXiv:2402.01138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01138},
    primaryClass={eess.SP cs.LG}
}