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A multi-domain graph convolutional network-based prediction model for personalized motor imagery action.

Jiahao Ge1, Jie Wang2, Xiao Zheng1,3,4

  • 1State Key Laboratory of Intelligent Power Distribution Equipment and System, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China.

Frontiers in Neuroscience
|November 14, 2025
PubMed
Summary

This study introduces a novel Multi-domain Graph Convolutional Network (M-GCN) to predict personalized motor imagery (MI) actions using cognitive EEG data. The M-GCN model achieved 73.60% accuracy, significantly improving brain-computer interface (BCI) personalization.

Keywords:
MI predictionbrain networkbrain-computer interfacecorrelation between cognitive tasks and MIfeature fusiongraph convolutional network

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Motor imagery (MI)-based brain-computer interfaces (BCIs) decode imagined actions.
  • Individual differences in MI are linked to cognitive EEG signals.
  • Predicting personalized MI actions is crucial for BCI efficacy.

Purpose of the Study:

  • To propose a Multi-domain Graph Convolutional Network (M-GCN) for personalized MI action prediction.
  • To leverage cognitive EEG data for enhanced MI action decoding.
  • To improve the accuracy and personalization of BCI systems.

Main Methods:

  • Developed an M-GCN model integrating time, frequency, and spatial EEG features.
  • Constructed multi-domain brain networks using various EEG quantization methods.
  • Employed spectral Graph Convolutional Network (GCN) to analyze functional connectivity.
  • Validated the model using a subject-independent, leave-one-subject-out cross-validation approach.

Main Results:

  • The M-GCN achieved a prediction accuracy of 73.60% for personalized MI actions.
  • Significantly outperformed baseline (15.87% improvement) and single-domain models (7.2% improvement).
  • Demonstrated the effectiveness of multi-domain feature fusion and GCN in BCI.

Conclusions:

  • The M-GCN accurately predicts personalized MI actions, enhancing BCI usability.
  • Multi-domain feature fusion based on cognitive tasks and GCN is highly effective.
  • This study offers a novel and efficient method for personalized BCI development.