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）越来越多地被用于脑电图（EEG）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。目前已有大量方法被提出来设计基于GNN的分类器，因此亟需对这些方法进行系统性综述与分类。我们针对这一主题进行了详尽的文献检索，并归纳出多个用于比较的分类维度。这些分类维度凸显了各方法间的异同。结果表明，谱域图卷积层比空域方法更为普遍。此外，我们识别了标准的节点特征形式，其中原始EEG信号和差分熵最为常用。我们的结果总结了基于GNN的EEG分类方法的新兴趋势。最后，我们讨论了若干有前景的研究方向，例如探索迁移学习方法的潜力以及恰当建模跨频率相互作用。