Li Han, Liu Ming, Yu Xin, Zhu JianQun, Wang Chongfeng, Chen Xinyi, Feng Chao, Leng Jiancai, Zhang Yang, Xu Fangzhou
International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China.
Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China.
Front Neurosci. 2023 Jan 13;16:1097660. doi: 10.3389/fnins.2022.1097660. eCollection 2022.
Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients.
According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.
The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.
The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.
脊髓损伤（SCI）可能导致运动功能受损、自主神经系统功能障碍及其他功能障碍。基于运动想象（MI）的脑机接口（BCI）系统可为SCI患者提供更科学有效的治疗方案。
根据脑区之间的相互作用，提出一种基于相干性的图卷积网络（C-GCN）方法，以提取脑电信号的时频空间特征和功能连接信息。该算法将基于相干网络构建的多通道脑电特征作为图形信号，然后对运动想象任务进行分类。与传统的图形卷积神经网络（GCN）不同，C-GCN方法利用脑电信号的相干网络来确定与运动想象相关的功能连接，这些连接用于表示不同节律和不同运动想象任务中脑电通道之间的内在联系。对SCI患者和健康受试者的脑电数据进行了分析，其中健康受试者作为对照组。
实验结果表明，C-GCN方法能够在一定的可靠性和稳定性下实现最佳分类性能，最高分类准确率为96.85%。
所提出的框架可为SCI患者的康复治疗提供有效的理论依据。
