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DGAT: a dynamic graph attention neural network framework for EEG emotion recognition.

Shihang Ding¹, Kaixuan Wang¹, Wenhao Jiang¹

¹Faculty of Computing, Harbin Institute of Technology, Harbin, China.

Frontiers in Psychiatry

|August 5, 2025

Summary

This study introduces a Dynamic Graph Attention Network (DGAT) for improved electroencephalogram (EEG) emotion recognition. DGAT enhances accuracy by dynamically learning channel relationships, outperforming existing models.

Keywords:

EEG affective computing dynamic graph attention network emotion recognition graph structure

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Area of Science:

Neuroscience
Artificial Intelligence
Signal Processing

Background:

Emotion recognition using electroencephalogram (EEG) signals is crucial for brain-computer interfaces and affective computing.
Existing graph neural network models face limitations due to fixed adjacency matrices, hindering adaptability and feature expressiveness.

Purpose of the Study:

To propose a novel framework, the Dynamic Graph Attention Network (DGAT), for enhanced EEG-based emotion recognition.
To overcome the limitations of fixed graph structures in current models.

Main Methods:

DGAT dynamically learns channel relationships using dynamic adjacency matrices.
A multi-head attention mechanism enables parallel computation and learning in different subspaces.
The framework reduces reliance on predefined adjacency structures.

Main Results:

DGAT achieved superior emotion classification accuracy on SEED and DEAP datasets.
The model demonstrated effectiveness in both subject-dependent and subject-independent scenarios.
DGAT successfully captures dynamic changes in EEG signals for improved recognition.

Conclusions:

The proposed DGAT model significantly enhances the accuracy and practicality of EEG emotion recognition.
DGAT holds substantial academic and practical value for analyzing emotional EEG and other physiological signals.