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Updated: May 30, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Graph convolution network-based eeg signal analysis: a review.

Hui Xiong1,2, Yan Yan3,4, Yimei Chen5

  • 1School of Control Science and Engineering, Tiangong University, Tianjin, 300387, China. xionghui@tiangong.edu.cn.

Medical & Biological Engineering & Computing
|January 30, 2025
PubMed
Summary
This summary is machine-generated.

This review explores Graph Convolutional Networks (GCNs) for analyzing Electroencephalography (EEG) signals, highlighting their applications in healthcare and brain-computer interfaces. It provides a systematic analysis of GCN methods and future research directions.

Keywords:
Brain-computer interfaceDeep learningElectroencephalographyGraph convolution networkGraph neural network

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Advancements in artificial intelligence (AI) drive new methods for Electroencephalography (EEG) signal analysis.
  • EEG signal analysis is crucial for healthcare and brain-computer interface (BCI) applications.
  • Graph Convolutional Networks (GCNs) show significant promise in processing complex EEG data.

Purpose of the Study:

  • To comprehensively review the applications and achievements of GCNs in EEG signal analysis.
  • To provide a module-by-module discussion of the current research status of GCNs in this field.
  • To identify key research issues and future development directions for GCNs in EEG analysis.

Main Methods:

  • Exhaustive literature search on GCNs for EEG signal analysis.
  • Systematic classification and analysis of GCN methods.
  • Detailed examination of key modules: brain map construction, node feature extraction, and GCN architecture design.

Main Results:

  • An in-depth review of GCN applications and achievements in EEG signal processing.
  • A structured classification of various GCN methodologies for EEG data.
  • Identification of critical research challenges and considerations for GCN implementation.

Conclusions:

  • GCNs offer substantial potential for advancing EEG signal analysis.
  • Future research should focus on GCN layer applicability, task-oriented models, and limited data adaptation.
  • This review provides valuable insights for researchers in AI, neuroscience, and BCI fields.