mlsanityData to 7 Oct 2026

Graph Neural Network-based EEG Classification: A Survey

arXiv 2310.02152 ↗First posted 3 Oct 2023q-bio.NC cs.LG q-bio.QMcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 35 of its 117 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

35 of 117 references.

References · 35

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23 more
  1. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
  2. 2019EEG-Based Emotion Recognition Using Regularized Graph Neural Networkscited by 97
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  4. 2021A Review on Graph Neural Network Methods in Financial Applicationscited by 77
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 5; 2026: 7.

21 papers.

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  1. 2025CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decodingcited by 46
  2. 2026LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosiscited by 6
  3. 2024Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptationcited by 4
  4. 2024GEFM: Graph-Enhanced EEG Foundation Modelcited by 4
  5. 2024A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspectivecited by 3
  6. 2024A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trendscited by 3
  7. 2026Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approachcited by 3
  8. 2024Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanationscited by 2
  9. 2024A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detectioncited by 2
  10. 2024Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusioncited by 2
  11. 2025Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodescited by 2
  12. 2024Balancing Spectral, Temporal and Spatial Information for EEG-based Alzheimer's Disease Classificationcited by 1
9 more
  1. 2025Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inferencecited by 1
  2. 2026LERD: Latent Event-Relational Dynamics for Neurodegenerative Classificationcited by 1
  3. 2024EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Networkcited by 0
  4. 2025Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognitioncited by 0
  5. 2025Seizure-NGCLNet: Representation Learning of SEEG Spatial Pathological Patterns for Epileptic Seizure Detection via Node-Graph Dual Contrastive Learningcited by 0
  6. 2026EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learningcited by 0
  7. 2026A Temporally Augmented Graph Attention Network for Affordance Classificationcited by 0
  8. 2026Connectivity Estimation using Stochastic Graph Heat Modellingcited by 0
  9. 2026ChatImage: Navigating Long-Form LLM Answers through Interactive Imagescited by 0