### Emotion Recognition of EEG Signal Based on Improved Convolutional Neural Network

TIAN Lili, ZOU Junzhong, ZHANG Jian, WEI Zuochen, WANG Chunmei

1. 1.Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
2.Department of Automation, School of Information Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
• Online:2019-11-15 Published:2019-11-13

### 基于改进的卷积神经网络脑电信号情感识别

1. 1.华东理工大学 信息科学与工程学院 自动化系，上海 200237
2.上海师范大学 信息与机电工程学院 自动化系，上海 200234

Abstract: Considering that traditional machine learning requires artificial construction features and low feature quality, this paper proposes a novel automatic feature extraction approach in Electroencephalograph（EEG） signals based on 1-D Convolutional Neural Network（CNN）. This approach uses the idea of compilation, at the same time the convolutional layer and the downsampling layer form the encoder network to extract the emotional characteristics of the EEG signal, then the Leaky ReLU activation function is applied to the feature map. For the convolution pre-training process, the cross-entropy and regularization terms are used to optimize the loss function, then the random forest classifier is used to obtain the emotion classification label. Finally, the experiment is carried out on the international public data set SEED, which achieves 94.7% sentiment classification accuracy, and the experimental results show the effectiveness and robustness of the proposed method.