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.

通过开发图神经网络 (GNN) 在基于脑电图的情绪识别领域中的独特应用，研究综述了现有方法，并提供了构建 GNN 在基于脑电图情绪识别中的清晰指导，同时探讨了一些挑战和未来方向。

基于脑电图的情绪识别中的图神经网络研究

Graph Neural Networks in EEG-based Emotion Recognition: A Survey

The progress of EEG-based emotion recognition has received widespread
attention from the fields of human-machine interactions and cognitive science
in recent years. However, how to recognize emotions with limited labels has
become a new research and application bottleneck. To address the issue, this
paper proposes a Self-supervised Group Meiosis Contrastive learning framework
(SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC,
a novel genetics-inspired data augmentation method, named Meiosis, is
developed. It takes advantage of the alignment of stimuli among the EEG samples
in a group for generating augmented groups by pairing, cross exchanging, and
separating. And the model adopts a group projector to extract group-level
feature representations from group EEG samples triggered by the same emotion
video stimuli. Then contrastive learning is employed to maximize the similarity
of group-level representations of augmented groups with the same stimuli. The
SGMC achieves the state-of-the-art emotion recognition results on the publicly
available DEAP dataset with an accuracy of 94.72% and 95.68% in valence and
arousal dimensions, and also reaches competitive performance on the public SEED
dataset with an accuracy of 94.04%. It is worthy of noting that the SGMC shows
significant performance even when using limited labels. Moreover, the results
of feature visualization suggest that the model might have learned video-level
emotion-related feature representations to improve emotion recognition. And the
effects of group size are further evaluated in the hyper parametric analysis.
Finally, a control experiment and ablation study are carried out to examine the
rationality of architecture. The code is provided publicly online.

本文提出了基于 SGMC 自监督分组减数对比学习框架的方法，采用 Meiosis 数据增强技术对基于同一情感视频刺激的人群 EEG 信号进行提取特征，使用对比学习来最大化不同组之间相似性，并在 DEAP 和 SEED 数据集上取得高准确性，同时有效地解决了有限标签的问题。