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CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer

Guoqing Cai¹, Yiyi Chen², Bolun Yang¹

¹School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.

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

|July 11, 2025

Summary

This summary is machine-generated.

This study introduces Complex-valued Graph Networks (CGNet) for electroencephalogram (EEG) brain-computer interfaces (BCIs). CGNet effectively integrates amplitude and phase information, significantly improving neural decoding accuracy for motor tasks.

Keywords:

Amplitude-phase learning Brain–computer interfaces Complex-valued computation Global dependency Graph convolutional network

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Area of Science:

Neuroscience
Computer Science
Signal Processing

Background:

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) are crucial for understanding neural processes.
Current deep learning methods often process EEG amplitude and phase independently, missing their synergistic interactions.

Purpose of the Study:

To develop a novel deep learning model that captures the combined information of amplitude and phase in EEG signals.
To enhance the performance of EEG-based BCIs by leveraging the interaction between neural signal amplitude and phase.

Main Methods:

Constructed a Complex-valued Graph Network (CGNet) to encode both amplitude and phase into a complex-valued representation.
Employed a two-scale complex-valued convolutional network, spatial attention, and dynamic graph convolution for spatio-temporal feature extraction.
Extended CGNet to Filter-Band CGNet (FBCGNet) for improved adaptability to broadband EEG data.

Main Results:

CGNet achieved state-of-the-art classification performance on motor imagery and execution BCI tasks.
FBCGNet demonstrated further performance improvements over CGNet.
Visualizations confirmed CGNet's ability to identify key spatio-temporal information relevant to BCI paradigms.

Conclusions:

CGNet effectively mines amplitude-phase information, offering more comprehensive neural decoding in EEG-based BCIs.
The proposed model outperforms methods using amplitude or phase alone.
CGNet represents a significant advancement for improving the accuracy and efficiency of BCIs.