Lv Renjie, Chang Wenwen, Yan Guanghui, Nie Wenchao, Zheng Lei, Guo Bin, Sadiq Muhammad Tariq
IEEE J Biomed Health Inform. 2025 Jan;29(1):210-223. doi: 10.1109/JBHI.2024.3464550. Epub 2025 Jan 7.
Motor imagery, as a paradigm of brain-computer interface, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-computer interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.
作为脑机接口的一种范式，运动想象在医学康复领域具有巨大潜力。针对脑电图（EEG）信号的非平稳性和低信噪比带来的挑战，从运动想象信号中有效提取特征以进行准确识别是运动想象脑机接口技术的关键重点。本文提出了一种将功能脑网络与图卷积网络相结合的运动想象EEG信号分类模型。首先，使用不同的脑功能连接指标构建功能脑网络，并计算图论特征以深入分析不同运动任务下脑网络的特征。然后，将构建的功能脑网络与图卷积网络相结合，用于运动想象任务的分类和识别。基于脑功能连接的分析表明，双拳任务期间的功能连接强度显著高于其他运动想象任务，实际运动期间的功能连接强度通常优于运动想象任务。在Physionet公共数据集上进行的实验中，所提出的模型在多受试者条件下实现了88.39%的分类准确率，显著优于传统方法。在单受试者条件下，该模型有效解决了个体变异性问题，实现了99.31%的平均分类准确率。这些结果表明，所提出的模型不仅在运动想象任务分类中表现出优异性能，还为不同运动任务及其相应脑区的功能连接特征提供了新的见解。
