  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.
