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CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition.

Jingjing Jia¹, Bofeng Zhang^2,3, Hehe Lv¹

¹School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

|July 27, 2022

Summary

This study introduces CR-GCN, a novel Graph Convolutional Network for emotion recognition using electroencephalography (EEG). CR-GCN enhances accuracy by effectively modeling relationships between EEG channels, outperforming existing methods.

Keywords:

CR-GCN adjacency matrix electroencephalography emotion recognition

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Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Electroencephalography (EEG) measures brain activity via electrodes.
EEG-based emotion recognition is a growing field.
Current EEG methods often neglect channel relationships, limiting recognition precision.

Purpose of the Study:

To propose a novel method, CR-GCN (Channel-Relationships-based Graph Convolutional Network), for improved EEG-based emotion recognition.
To address the limitations of existing methods by fully exploiting EEG channel relationships.

Main Methods:

Developed CR-GCN, a Graph Convolutional Network model.
Constructed an adjacency matrix to capture both local (distance-based) and global (functional connectivity) relationships among EEG channels.
Utilized extensive experiments to validate the proposed method.

Main Results:

CR-GCN significantly outperformed state-of-the-art methods in EEG-based emotion recognition.
Achieved high average classification accuracies: 94.69% (valence) and 93.95% (arousal) in subject-dependent tests.
Obtained excellent average classification accuracies: 94.78% (valence) and 93.46% (arousal) in subject-independent tests.

Conclusions:

The proposed CR-GCN method effectively models EEG channel relationships for enhanced emotion recognition.
CR-GCN demonstrates superior performance compared to existing approaches.
The findings highlight the importance of considering both local and global channel interactions in EEG analysis for emotion recognition.