Graph Neural Networks in EEG-based Emotion Recognition: A Survey
- URL: http://arxiv.org/abs/2402.01138v1
- Date: Fri, 2 Feb 2024 04:30:58 GMT
- Title: Graph Neural Networks in EEG-based Emotion Recognition: A Survey
- Authors: Chenyu Liu, Xinliang Zhou, Yihao Wu, Ruizhi Yang, Liming Zhai, Ziyu
Jia and Yang Liu
- Abstract summary: A significant trend is to develop Graph Neural Networks (GNNs) for EEG-based emotion recognition.
Brain region dependencies in emotional EEG have physiological bases that distinguish GNNs in this field from those in other time series fields.
We analyze and categorize methods from three stages in the framework to provide clear guidance on constructing GNNs in EEG-based emotion recognition.
- Score: 8.727911746686848
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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