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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
Summary
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.
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.
