GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

@article{Lun2022GCNsNetAG,
  title={GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals},
  author={Xiangmin Lun and Shuyue Jia and Yimin Hou and Yan Shi and Y. Li and Hanrui Yang and Shu Zhang and Jinglei Lv},
  journal={IEEE transactions on neural networks and learning systems},
  year={2022},
  volume={PP}
}
  • Xiangmin Lun, Shuyue Jia, J. Lv
  • Published 16 June 2020
  • Computer Science
  • IEEE transactions on neural networks and learning systems
Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To… 

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