Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition

TL;DR

This paper introduces APAGNN, a novel neural network that adaptively analyzes EEG data through hierarchical experts to improve emotion recognition accuracy across multiple datasets.

Contribution

The paper presents a new adaptive graph neural network with a hierarchical expert structure for more effective EEG emotion recognition.

Findings

Achieves superior accuracy on SEED, SEED-IV, and MPED datasets.

Effectively captures spatial brain relationships during emotional processing.

Outperforms baseline methods in EEG emotion recognition.

Abstract

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.