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Summary

This summary is machine-generated.

Graph neural networks (GNNs) are advancing EEG classification for various applications. This review categorizes GNN methods, finding spectral layers and specific node features like raw EEG signals are most common.

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Area of Science:

Neuroscience
Computer Science
Machine Learning

Background:

Graph neural networks (GNNs) are gaining traction for electroencephalogram (EEG) classification.
Applications include emotion recognition, motor imagery, and diagnosing neurological disorders.

Purpose of the Study:

To systematically review and categorize existing GNN-based approaches for EEG classification.
To identify trends and common methodologies in the field.

Main Methods:

Exhaustive literature search on GNNs for EEG classification.
Categorization of identified methods for comparative analysis.

Main Results:

A prevalence of spectral graph convolutional layers over spatial ones was observed.
Common node features include raw EEG signals and differential entropy.

Conclusions:

Summarizes current trends in GNN-based EEG classification.
Highlights future research directions like transfer learning and cross-frequency interaction modeling.