Graph Neural Networks in EEG-based Emotion Recognition: A Survey
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912940543705088 |
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| author | Liu, Chenyu Deng, Yuqiu Wu, Yihao Yang, Ruizhi Wang, Zhongruo Zhang, Liangwei Chen, Siyun Zhang, Tianyi Liu, Yang Ding, Yi Zhai, Liming Jia, Ziyu Zhou, Xinliang |
| author_facet | Liu, Chenyu Deng, Yuqiu Wu, Yihao Yang, Ruizhi Wang, Zhongruo Zhang, Liangwei Chen, Siyun Zhang, Tianyi Liu, Yang Ding, Yi Zhai, Liming Jia, Ziyu Zhou, Xinliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks in EEG-based Emotion Recognition: A Survey Liu, Chenyu Deng, Yuqiu Wu, Yihao Yang, Ruizhi Wang, Zhongruo Zhang, Liangwei Chen, Siyun Zhang, Tianyi Liu, Yang Ding, Yi Zhai, Liming Jia, Ziyu Zhou, Xinliang Signal Processing Machine Learning 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. |
| title | Graph Neural Networks in EEG-based Emotion Recognition: A Survey |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2402.01138 |