App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Graph neural classification model with fine-grained graph convolutional feature extraction for fine ARM motor imagery

Li Zexu
He Hong
Chen Yucong
Xu Chudi
School of Health Science & Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0018

Abstract

Existing methods face challenges in fully capturing the complex spatiotemporal coupling features of motor imagery EEG signals from fine arm movements, and there remains room for improvement in their decoding precision and accuracy. Focusing on these challenges, this study completed the construction of an EEG database for arm motor imagery, and proposed a fine-grained graph convolutional classification model for motor imagery EEG signals. The model embedded a self-attention mechanism to focus on core electrode channels, and accurately captured deep dependencies between nodes to suppress redundant interference. This study designed a fine-grained graph neural module. Through time window segmentation and multi-scale subgraph construction, the module alleviated the limitation of graph neural networks that tend to prioritize spatial information over temporal characteristics. The adopted convolutional classification layer efficiently completed the integration and discrimination of spatiotemporal features. This study conducted comparative experiments based on the self-built arm movement imagery dataset and public datasets. Experimental results showed that the model achieved favorable classification performance compared with multiple mainstream baseline classification models, with a maximum within-subject accuracy of 76% and a maximum cross-subject accuracy of 59% on the arm movement dataset. The proposed model architecture proves effective. It provides a new perspective for the spatiotemporal joint modeling of motor imagery in complex EEG signals, and offers technical support for the development of high-performance brain-computer interface systems.

Foundation Support

国家科学技术部项目(G2021013008)
教育部中国高校产学研创新基金资助项目(2023RY011)
上海理工大学医工交叉重点创新项目
华为AI算力加速计划项目(1022308502)
上海理工大学教师发展研究重点项目(CFTD2025ZD08)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0018
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-06-02] Accepted Paper

Cite This Article

李泽旭, 何宏, 陈宇聪, 等. 一种针对手臂精细动作想象的细粒度图神经分类模型 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0018. (Li Zexu, He Hong, Chen Yucong, et al. Graph neural classification model with fine-grained graph convolutional feature extraction for fine ARM motor imagery [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0018. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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