# A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective

arXiv: https://arxiv.org/abs/2408.06027 · HTML: https://mlsanity.com/paper/2408.06027/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 99 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [XGNN: Towards Model-Level Explanations of Graph Neural Networks](https://mlsanity.com/paper/2006.02587/) (2020)
- [Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking](https://mlsanity.com/paper/2010.00577/) (2020)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2108.03354/) (2021)
- [Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback](https://mlsanity.com/paper/1909.05764/) (2019)

## Built on by (most cited first, 4 of 4)

- [MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition](https://mlsanity.com/paper/2507.15914/) (2025)
- [Fuzzy Information Evolution with Three-Way Decision in Social Network Group Decision-Making](https://mlsanity.com/paper/2505.16781/) (2025)
- [EEG-based Graph-guided Domain Adaptation for Robust Cross-Session Emotion Recognition](https://mlsanity.com/paper/2512.23526/) (2025)
- [DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models](https://mlsanity.com/paper/2610.14497/) (2026)

## Abstract (arXiv)

Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one of the most focused tasks in affective computing. The nature of emotions is a physiological and psychological state change in response to brain region connectivity, making emotion recognition focus more on the dependency between brain regions instead of specific brain regions. A significant trend is the application of graphs to encapsulate such dependency as dynamic functional connections between nodes across temporal and spatial dimensions. Concurrently, the neuroscientific underpinnings behind this dependency endow the application of graphs in this field with a distinctive significance. However, there is neither a comprehensive review nor a tutorial for constructing emotion-relevant graphs in EEG-based emotion recognition. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of graph-related methods in this field from a methodological perspective. We propose a unified framework for graph applications in this field and categorize these methods on this basis. Finally, based on previous studies, we also present several open challenges and future directions in this field.