ASTGNN: adaptive spatio-temporal graph neural network for motor imagery recognition

Zheng Zhao; Haixiao Xue; Yuwen Huang; Qi Zhao; Xin Xie

doi:10.1117/12.3051886

16 January 2025 ASTGNN: adaptive spatio-temporal graph neural network for motor imagery recognition

Zheng Zhao, Haixiao Xue, Yuwen Huang, Qi Zhao, Xin Xie

Author Affiliations +

Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 134470A (2025) https://doi.org/10.1117/12.3051886
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China

Abstract

Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the brain's topological characteristics and the correlations between EEG channels, resulting in suboptimal decoding. To address this, we propose an adaptive spatio-temporal graph neural network (ASTGNN), which constructs a brain graph topology and adaptively learns the topological adjacency matrix, exploring both common and individual electrode connections. The spatial features are extracted using a graph convolutional network, while a gated position-aware self-attention mechanism captures movement information and global dependencies, enhancing temporal feature extraction. Experiments show that ASTGNN significantly improves recognition, achieving an average accuracy of 87.36%.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheng Zhao, Haixiao Xue, Yuwen Huang, Qi Zhao, and Xin Xie "ASTGNN: adaptive spatio-temporal graph neural network for motor imagery recognition", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 134470A (16 January 2025); https://doi.org/10.1117/12.3051886

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