# Bridging Neural Topology and Affective Computing: Graph Attention for EEG Emotion Recognition

## TL;DR

This paper reviews how graph attention networks improve emotion recognition from EEG data by modeling brain connectivity patterns.

## Contribution

The paper introduces a standardized framework to improve reproducibility in graph-based EEG emotion recognition models.

## Key findings

- Graph attention mechanisms preserve brain topology while emphasizing emotion-relevant regions.
- Standardized preprocessing and validation protocols enhance model performance and reproducibility.
- Future research should focus on model efficiency and multimodal integration for better generalization.

## Abstract

Electroencephalography (EEG) offers high temporal resolution and strong physiological validity for emotion recognition. However, complex spatial organization and inter-subject variability present major modeling challenges. Graph-based spatial attention mechanisms have emerged as a key solution, preserving brain topological priors while adaptively emphasizing emotion-relevant regions and connections. This review summarizes advances in graph convolutional networks (GCN) and graph attention networks (GAT), covering representative studies under both subject-dependent and subject-independent settings. In architectural innovations, this paper critically evaluates the implicit impact of experimental factors, including preprocessing pipelines and validation protocols, on performance, and proposes a standardized framework to enhance reproducibility. Existing research demonstrates progressive transitions from static to dynamic graphs and from single-domain to multimodal fusion guided by physiological priors. Future research is expected to focus on enhancing model efficiency, strengthening neurophysiological alignment, integrating multimodal information and enhancing subject-independent generalization, and extending applications to affective neuroscience and clinical contexts. These developments collectively drive EEG-based emotion recognition toward more efficient, interpretable, and translationally valuable affective computing systems.