# PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2302.02520 · HTML: https://mlsanity.com/paper/2302.02520/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2023.

## Built on by (most cited first, 15 of 15)

- [Long-range Brain Graph Transformer](https://mlsanity.com/paper/2501.01100/) (2025)
- [LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition](https://mlsanity.com/paper/2410.09767/) (2024)
- [MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition](https://mlsanity.com/paper/2507.15914/) (2025)
- [Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework](https://mlsanity.com/paper/2505.18175/) (2025)
- [FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2506.22807/) (2025)
- [A novel Fourier Adjacency Transformer for advanced EEG emotion recognition](https://mlsanity.com/paper/2503.13465/) (2025)
- [SynMind: Reducing Semantic Hallucination in fMRI-Based Image Reconstruction](https://mlsanity.com/paper/2601.17857/) (2026)
- [FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2503.18998/) (2025)
- [Graph-Based Learning of Spectro-Topographical EEG Representations with Gradient Alignment for Brain-Computer Interfaces](https://mlsanity.com/paper/2512.07820/) (2025)
- [TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2606.03998/) (2026)
- [RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning](https://mlsanity.com/paper/2606.15278/) (2026)
- [Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition](https://mlsanity.com/paper/2607.04139/) (2026)
- [FSDBN: Foreground-Aware EEG-Visual Alignment via Dynamic Brain Networks](https://mlsanity.com/paper/2607.18344/) (2026)
- [Mind2Cloud: EEG-to-Point Cloud Generation with Two-Granularity Diffusion Decoding](https://mlsanity.com/paper/2609.13991/) (2026)
- [Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures](https://mlsanity.com/paper/2609.22103/) (2026)

## Abstract (arXiv)

Emotion recognition is essential in the diagnosis and rehabilitation of various mental diseases. In the last decade, electroencephalogram (EEG)-based emotion recognition has been intensively investigated due to its prominative accuracy and reliability, and graph convolutional network (GCN) has become a mainstream model to decode emotions from EEG signals. However, the electrode relationship, especially long-range electrode dependencies across the scalp, may be underutilized by GCNs, although such relationships have been proven to be important in emotion recognition. The small receptive field makes shallow GCNs only aggregate local nodes. On the other hand, stacking too many layers leads to over-smoothing. To solve these problems, we propose the pyramidal graph convolutional network (PGCN), which aggregates features at three levels: local, mesoscopic, and global. First, we construct a vanilla GCN based on the 3D topological relationships of electrodes, which is used to integrate two-order local features; Second, we construct several mesoscopic brain regions based on priori knowledge and employ mesoscopic attention to sequentially calculate the virtual mesoscopic centers to focus on the functional connections of mesoscopic brain regions; Finally, we fuse the node features and their 3D positions to construct a numerical relationship adjacency matrix to integrate structural and functional connections from the global perspective. Experimental results on three public datasets indicate that PGCN enhances the relationship modelling across the scalp and achieves state-of-the-art performance in both subject-dependent and subject-independent scenarios. Meanwhile, PGCN makes an effective trade-off between enhancing network depth and receptive fields while suppressing the ensuing over-smoothing. Our codes are publicly accessible at https://github.com/Jinminbox/PGCN.