【摘要】 学生的学习情况与其课堂注意力状态密切相关。为了探寻注意力训练能否提高课堂注意力，对10名在校学生进行了α音乐训练，并收集了训练前后的非注意和注意状态的脑电（Electroencephalogram,EEG）信号进行对比研究。由于EEG信号本质上是动态的，且具有低信噪比和高冗余度的特性，为避免直接通过神经网络识别EEG信号效果差的问题，提取了信号的样本熵（Sample entropy,SampEn）、各个波段的能量和能量比共11个特征，并将这些特征进行融合转化为多特征图像，作为神经网络模型的输入。此外，将AlexNet和VGG11两个网络模型进行加权融合构成双卷积神经网络，进一步提高了图像分类性能。结果表明，与单个模型相比，双卷积神经网络融合模型的性能更佳，其识别准确率最高可达到97.53%。研究发现，经过α音乐训练，受试者的脑电特征与此前相比有显著性差异，且网络模型的分类准确率比训练前提高了4%，说明本文所提的α音乐训练能够提高健康学生的注意力水平。更多还原

【Abstract】 Students’ learning situation is closely related to their classroom attention state. In order to explore whether attention training can improve classroom attention,the electroencephalogram(EEG)signals of non-attention and attention states of ten students before and after α music training are collected and compared. It is worth noting that EEG signal is dynamic in nature and has the characteristics of low signal-to-noise ratio and high redundancy. In order to avoid the problem of poor recognition of EEG signals directly through neural network,11 features of signal sample entropy(SampEn),energy and energy ratio of each band are extracted,and these features are fused into multi-feature images as the input of neural network model. In addition,the weighted fusion of AlexNet and VGG11 network models is used to form a double convolution neural network(CNN), which can further improve the performance of image classification. The results show that the performance of the fusion model with double CNN can achieve a better performance compared with the model with single CNN. In particular,the recognition accuracy of the proposed model can reach 97.53%. It can be found that after α music training,the EEG features of the subjects are significantly different from those before,and the classification accuracy of the network model can be 4% higher than that before training. This observations show that the considered α music training can improve the attention level of healthy students.更多还原