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, ChinaChen Du
School of Electronic and Optical Engineering, 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
TP391
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.
