# Graph Neural Network-based EEG Classification: A Survey

arXiv: https://arxiv.org/abs/2310.02152 · HTML: https://mlsanity.com/paper/2310.02152/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 117 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 30 of 35)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions](https://mlsanity.com/paper/2109.12843/) (2021)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Topology-Aware Graph Pooling Networks](https://mlsanity.com/paper/2010.09834/) (2020)
- [Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification](https://mlsanity.com/paper/2109.01824/) (2021)
- [LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface](https://mlsanity.com/paper/2105.02786/) (2021)
- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals](https://mlsanity.com/paper/2106.09135/) (2021)
- [A Survey on Spectral Graph Neural Networks](https://mlsanity.com/paper/2302.05631/) (2023)
- [Temporal Graph Convolutional Networks for Automatic Seizure Detection](https://mlsanity.com/paper/1905.01375/) (2019)
- [iPool -- Information-based Pooling in Hierarchical Graph Neural Networks](https://mlsanity.com/paper/1907.00832/) (2019)
- [GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals](https://mlsanity.com/paper/2006.08924/) (2020)
- [Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks](https://mlsanity.com/paper/2002.11867/) (2020)
- [EEG-based video identification using graph signal modeling and graph convolutional neural network](https://mlsanity.com/paper/1809.04229/) (2018)
- [Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data](https://mlsanity.com/paper/2304.05874/) (2023)
- [DAGAM: A Domain Adversarial Graph Attention Model for Subject Independent EEG-Based Emotion Recognition](https://mlsanity.com/paper/2202.12948/) (2022)

## Built on by (most cited first, 21 of 21)

- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis](https://mlsanity.com/paper/2604.28178/) (2026)
- [A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective](https://mlsanity.com/paper/2408.06027/) (2024)
- [Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2409.19635/) (2024)
- [GEFM: Graph-Enhanced EEG Foundation Model](https://mlsanity.com/paper/2411.19507/) (2024)
- [A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends](https://mlsanity.com/paper/2409.19629/) (2024)
- [Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach](https://mlsanity.com/paper/2604.01595/) (2026)
- [Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations](https://mlsanity.com/paper/2408.00906/) (2024)
- [A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection](https://mlsanity.com/paper/2410.18103/) (2024)
- [Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion](https://mlsanity.com/paper/2411.06917/) (2024)
- [Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodes](https://mlsanity.com/paper/2601.00012/) (2025)
- [Balancing Spectral, Temporal and Spatial Information for EEG-based Alzheimer's Disease Classification](https://mlsanity.com/paper/2402.13523/) (2024)
- [Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference](https://mlsanity.com/paper/2505.16893/) (2025)
- [LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification](https://mlsanity.com/paper/2602.18195/) (2026)
- [EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network](https://mlsanity.com/paper/2412.17834/) (2024)
- [Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2501.14246/) (2025)
- [Seizure-NGCLNet: Representation Learning of SEEG Spatial Pathological Patterns for Epileptic Seizure Detection via Node-Graph Dual Contrastive Learning](https://mlsanity.com/paper/2512.02028/) (2025)
- [EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning](https://mlsanity.com/paper/2603.16895/) (2026)
- [A Temporally Augmented Graph Attention Network for Affordance Classification](https://mlsanity.com/paper/2604.10149/) (2026)
- [Connectivity Estimation using Stochastic Graph Heat Modelling](https://mlsanity.com/paper/2606.29098/) (2026)
- [ChatImage: Navigating Long-Form LLM Answers through Interactive Images](https://mlsanity.com/paper/2607.05290/) (2026)

## 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.