1.南京邮电大学电子与光学工程学院 南京 210023; 2.南京邮电大学射频集成与微组装技术国家地方联合工程实验室 南京 210023; 3.南京邮电大学江苏省邮政大数据技术与应用工程研究中心 南京 210003; 4.南京邮电大学国家邮政局邮政行业技术研发中心(物联网技术) 南京 210003
TP391
国家自然科学基金（61972208，61672299）项目资助
Zhang Xuejun
1. School of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; 2. Nation-Local Joint Project Engineering Lab of RF Integration & Micropackage, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSun Zhixin
3. Post Big Data Technology and Application Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; 4. Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing 210003, China1. School of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; 2. Nation-Local Joint Project Engineering Lab of RF Integration & Micropackage, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; 3. Post Big Data Technology and Application Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; 4. Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing 210003, China
情绪作为人脑的高级功能，对人们的心理健康和个性特征有很大的影响。通过对脑电情绪数据集进行情绪分类，能够为今后实时监控正常人或抑郁病人的情绪提供进一步理论及实践依据。因此文章运用公开的脑电情绪数据集所提取的微分熵特征，并使用传统的滑动平均和线性动态系统方法，采用深度学习中的卷积神经网络作为基本前提，设计了一个卷积神经网络的脑电信号情绪分类模型，其包括4个卷积层、4个最大池化层、2个全连接层和1个Softmax层，并采用批归一化使参数搜索问题变容易，抑制模型过拟合。实验结果表明，利用该模型对SEED数据集的3种情绪识别的平均准确率达到了98.73%，精确率、召回率和F1分数分别为99.69%、98.12%和98.86%，ROC曲线下面积达0.998。与最近的类似工作相比，该文提出的卷积神经网络结构对于脑电信号情绪分类具有一定优越性。
As an advanced function of the human brain, emotion has a great impact on people's mental health and personality characteristics.The classification of EEG emotion data sets can provide further theoretical and practical basis for real-time monitoring of normal and depressed patients' emotions in the future. The article uses the differential entropy features extracted from the public EEG emotion data set, and uses traditional moving average and linear dynamic system methods. Using the convolutional neural network in deep learning as the basic premise, a convolutional neural network's EEG signal emotion classification model is designed, which includes 4 convolutional layers, 4 maximum pooling layers, 2 fully connected layers, and 1 A Softmax layer, and batch normalization is used to make the parameter search problem easier and suppress the model over-fitting. The experimental results show that the average accuracy of the three emotion recognition of the SEED data set using this model reached 98.73%, the precision, recall and F1 score were 99.69%, 98.12% and 98.86%, respectively, and the area under the ROC curve reached 0.998. Compared with recent similar work, the convolutional neural network structure proposed in this paper has certain advantages for EEG signal emotion classification.
张学军,陈都,孙知信.基于卷积神经网络的脑电信号情绪分类方法[J].电子测量技术,2022,45(1):1-7
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