Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.

翻译：图神经网络（GNN）正越来越多地被用于对脑电（EEG）信号进行分类，应用于情绪识别、运动想象以及神经系统疾病与障碍等任务。目前已提出了大量基于GNN的分类器设计方法。因此，有必要对这些方法进行系统性的回顾与分类。我们对该主题下的已发表文献进行了详尽检索，并归纳出若干比较类别。这些类别突出了不同方法间的共性与差异。结果表明，谱图卷积层在应用上比空间图卷积层更为普遍。此外，我们识别出典型的节点特征形式，其中原始EEG信号和差分熵最为常用。我们的总结揭示了基于GNN的EEG分类方法的新兴趋势。最后，我们讨论了若干有价值的研究方向，例如探索迁移学习方法的潜力以及跨频交互的合理建模。