Graph Neural Network-based EEG Classification: A Survey

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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.

Original languageEnglish
Pages (from-to)493-503
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication statusPublished - 18 Jan 2024

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see


This work was supported by the Agency for Science, Technology and Research (A*STAR), AI, Analytics and Informatics (AI3) Horizontal Technology Program Office (HTPO) under Grant C211118015, and in part by the Engineering and Physical
Sciences Research Council (EPSRC) under Grant EP/X020193/1.


  • Graph neural network
  • classification
  • EEG
  • neuroscience
  • deep learning

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering
  • Rehabilitation


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