# Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2501.14246 · HTML: https://mlsanity.com/paper/2501.14246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.14862/) (2021)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition](https://mlsanity.com/paper/1906.01704/) (2019)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [Sharpen Focus: Learning with Attention Separability and Consistency](https://mlsanity.com/paper/1811.07484/) (2018)
- [GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition](https://mlsanity.com/paper/2205.01030/) (2022)
- [Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal](https://mlsanity.com/paper/2305.19379/) (2023)
- [Grad-CAM guided channel-spatial attention module for fine-grained visual classification](https://mlsanity.com/paper/2101.09666/) (2021)
- [CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition](https://mlsanity.com/paper/2305.05548/) (2023)

## Abstract (arXiv)

In recent years, numerous neuroscientific studies demonstrate that specific areas of the brain are connected to human emotional responses, with these regions exhibiting variability across individuals and emotional states. To fully leverage these neural patterns, we propose an Adaptive Progressive Attention Graph Neural Network (APAGNN), which dynamically captures the spatial relationships among brain regions during emotional processing. The APAGNN employs three specialized experts that progressively analyze brain topology. The first expert captures global brain patterns, the second focuses on region-specific features, and the third examines emotion-related channels. This hierarchical approach enables increasingly refined analysis of neural activity. Additionally, a weight generator integrates the outputs of all three experts, balancing their contributions to produce the final predictive label. Extensive experiments conducted on SEED, SEED-IV and MPED datasets indicate that our method enhances EEG emotion recognition performance, achieving superior results compared to baseline methods.