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    张效艇, 陈兰岚, 陈长德. 基于图卷积神经网络的脑力负荷识别[J]. 华东理工大学学报(自然科学版), 2023, 49(6): 882-889. DOI: 10.14135/j.cnki.1006-3080.20220812001
    引用本文: 张效艇, 陈兰岚, 陈长德. 基于图卷积神经网络的脑力负荷识别[J]. 华东理工大学学报(自然科学版), 2023, 49(6): 882-889. DOI: 10.14135/j.cnki.1006-3080.20220812001
    ZHANG Xiaoting, CHEN Lanlan, CHEN Changde. Recognition of Mental Workload Based on Graph Convolutional Neural Network[J]. Journal of East China University of Science and Technology, 2023, 49(6): 882-889. DOI: 10.14135/j.cnki.1006-3080.20220812001
    Citation: ZHANG Xiaoting, CHEN Lanlan, CHEN Changde. Recognition of Mental Workload Based on Graph Convolutional Neural Network[J]. Journal of East China University of Science and Technology, 2023, 49(6): 882-889. DOI: 10.14135/j.cnki.1006-3080.20220812001

    基于图卷积神经网络的脑力负荷识别

    Recognition of Mental Workload Based on Graph Convolutional Neural Network

    • 摘要: 针对生理信号特征提取过程依赖于先验知识,且传统深度学习算法不考虑通道间耦合关系的问题,构建了基于图卷积神经网络的脑力负荷识别模型。数据集包含多通道连续采集的近红外光谱,分别计算基于互信息、锁相值和皮尔森相关系数的连通性矩阵来反映通道间的内在联系,并将近红外光谱和连通性矩阵组成图结构输入到图卷积神经网络。实验结果表明,该模型具有良好的抽象特征提取能力,在输入中融合通道间相关性系数有助于提升脑力负荷的识别精度,且连通性矩阵的可视化结果表明大脑额叶区对脑力负荷变化较敏感。

       

      Abstract: Mental workload can be viewed as the utilization rate of brain resources in a task, which reflects the mental state of humans in their work. The accurate recognition of mental workload can help the system adjust the operator’s work tasks, and can be applied to a series of complex human-computer interaction systems such as remote surgery, driving monitoring, equipment maintenance, and so on. In many physiological signals, near infrared spectroscopy (NIRS) is insensitive to electrical noise and has good resistance to motion artifacts. Moreover, the spatial resolution of NIRS is higher than that of electroencephalogram (EEG). Hence, NIRS can better reveal the relationship between local brain areas and mental workload. The proposed mental workload recognition model based on multi-channel NIRS is motivated from two aspects: (1) The traditional feature extraction process of physiological signals largely depends on prior knowledge; (2) Most deep learning models do not consider the relationship between channels. The connectivity matrices based on mutual information, phase-locked value, and Pearson correlation coefficient are calculated to reflect the internal relationship between channels. Then, the original NIRS signals and the connectivity matrix form a graph structure, which is sent to the graph convolution neural network for training and testing. The experimental results show that the graph convolution neural network has good ability to extract abstract features. The recognition accuracy of mental workload can be further improved with the consideration of the correlation coefficient between channels. Moreover, the visualization results of connectivity matrix show that the frontal lobe of the brain is sensitive to changes in mental workload.

       

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