摘要Sleep is an essential integrant in everyone's daily life;therefore,it is an important but challenging problem to characterize sleep stages from electroencephalogram(EEG)signals.The network motif has been developed as a useful tool to investigate complex networks.In this study,we developed a multiplex visibility graph motif-based convolutional neural network(CNN)for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages.The independent samples t-test shows that the multiplex motif entropy values have significant differences among the six sleep stages.Furthermore,we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages.Notably,the classification accuracy of the six-state stage detection was 85.27％.Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages,whereby they further provide an essential strategy for future sleep-stage detection research.