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Minimum spanning tree based graph neural network for emotion classification using EEG.

Hanjie Liu¹, Jinren Zhang¹, Qingshan Liu¹

¹School of Mathematics, Southeast University, Nanjing 210096, China; Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, Southeast University, Nanjing 210096, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 18, 2021

Summary

This study introduces a novel graph neural network to analyze brain network structures for emotion classification using neurophysiology signals. The method effectively identifies distinct minimum spanning tree (MST) topologies related to different emotions.

Keywords:

DEAP Emotion classification Graph neural network MST

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Area of Science:

Neuroscience
Machine Learning
Affective Computing

Background:

Emotion classification from neurophysiology signals is challenging.
Brain network structure offers insights into human emotions.

Purpose of the Study:

To propose a novel method for capturing distinct minimum spanning tree (MST) topologies for emotion recognition.
To investigate MST structures in emotion recognition using graph neural networks.

Main Methods:

Developed a hierarchical aggregation-based graph neural network.
Analyzed minimum spanning tree (MST) topology from neurophysiology signals.
Utilized the public DEAP dataset for experiments.

Main Results:

The proposed model demonstrated superior performance in emotion classification compared to existing methods.
Identified theta, lower beta, and gamma frequency bands as sensitive to emotional states.
Suggested a multi-frequency interaction in emotion processing.

Conclusions:

The novel graph neural network approach effectively captures emotion-specific brain network structures.
The findings highlight the importance of multi-frequency band interactions in emotion processing.
This method advances the field of neurophysiological emotion recognition.