计算机应用研究2026，Vol.43Issue(9)：2648-2655,8.DOI:10.19734/j.issn.1001-3695.2026.01.0018
一种针对手臂精细动作想象的细粒度图神经分类模型
Graph neural classification model with fine-grained graph convolutional feature extraction for fine arm motor imagery
摘要
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.Fo-cusing 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 redun-dant 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 discrimina-tion of spatiotemporal features.This study conducted comparative experiments based on the self-built arm movement imagery dataset and public datasets.Experimental results show that the model achieves 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.These results confirm that the proposed model architecture is ef-fective,provide a new perspective for the spatiotemporal joint modeling of motor imagery in complex EEG signals,and offer technical support for the development of high-performance brain-computer interface systems.关键词
动作想象/脑电/图神经/手臂动作想象/信号分类Key words
motor imagery/electroencephalography/graph convolutional network/arm movement imagery/signal classifica-tion分类
信息技术与安全科学引用本文复制引用
李泽旭,何宏,陈宇聪,徐楚迪..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):2648-2655,8.基金项目
国家科学技术部项目(G2021013008) （G2021013008）
教育部中国高校产学研创新基金资助项目(2023RY011) （2023RY011）
上海理工大学医工交叉重点创新项目 （）
华为AI算力加速计划资助项目(1022308502) （1022308502）
上海理工大学教师发展研究重点项目(CFTD2025ZD08) （CFTD2025ZD08）
