A novel mothed for EEG motor imagery classification with graph convolutional network

Zongfu Qu; Zhigang Yin; Luo Yang

doi:10.1117/12.2655824

12 January 2023 A novel mothed for EEG motor imagery classification with graph convolutional network

Zongfu Qu, Zhigang Yin, Luo Yang

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125092S (2023) https://doi.org/10.1117/12.2655824
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

A motor imagery brain-computer interface system with practical application value should be able to show stable performance when facing new users. The distribution of electrodes on the cerebral cortex is the same for any user. Therefore, in order to solve the subject-independent problem, we propose a novel Graph Convolutional Convolution Transformer Net (GCCTN), which uses a graph convolutional neural network to calculate the relationship between an electrode and other electrodes, uses a convolutional neural network to extract temporal and spatial information and uses a Transformer Encoder for further extraction of time-domain information. Finally, the classification accuracy of our model is optimal.

Citation Download Citation

Zongfu Qu, Zhigang Yin, and Luo Yang "A novel mothed for EEG motor imagery classification with graph convolutional network", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125092S (12 January 2023); https://doi.org/10.1117/12.2655824

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