Spatial-temporal graph convolutional attentional network for motor imagery EEG classification

To address the challenge of insufficient capture of learnable functional connectivity and discriminative spatial-temporal features in motor imagery EEG (MI-EEG) decoding, this paper proposes a Spatial-Temporal Graph Convolutional Attentional Network (STGCAN-MI). The method first explicitly models the functional connectivity and temporal dependencies among electrodes through a learnable dynamic graph convolution module. It then introduces a lightweight parallel spatial-temporal attention module to perform adaptive recalibration of feature responses across channel and time dimensions, thereby enhancing critical discriminative information and suppressing irrelevant noise.

Experiments on the BCI Competition IV dataset 2a (four-class) and 2b (binary-class) show that STGCAN-MI achieves average best classification accuracies of 83.17% and 87.50%, respectively, outperforming several representative baseline models. Ablation studies and visualization analysis validate the effectiveness of each core component. This research provides a new approach for developing high-performance decoding methods for motor imagery EEG.

This work was supported by the National Key Research and Development Program of China under Grant 2023YFF204203. Zhang have contributed equally to this work. Institute of Artificial Intelligence in Agriculture and Forestry, College of Computer and Information Sciences, College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China Xianqing Yang, Tingting Zhang, Minghui Hua, Ruiqing Li, Guoyu Hu & Xiangzeng Kong College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China Zijian Li, Haorong Liao, Zhicong Luo & Junfeng Han College of Physical Education, Fujian Normal University, Fuzhou, 350117, China Correspondence to Fan Wang, Guoyu Hu or Xiangzeng Kong.

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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Yang, X., Zhang, T., Li, Z. et al. Spatial-temporal graph convolutional attentional network for motor imagery EEG classification.