Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (5): 161-167.

### EEG Emotion Recognition Using Convolutional Neural Network with 3D Input

CAI Dongli, ZHONG Qinghua, ZHU Yongsheng, LIAO Jinxiang, HAN Maizhi

1. School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
• Online:2021-03-01 Published:2021-03-02

### 三维输入卷积神经网络脑电信号情感识别

1. 华南师范大学 物理与电信工程学院，广州 510006

Abstract:

In order to preserve the spatial information between the electrodes and fully extract the characteristics of Electroencephalogram（EEG） and improve the accuracy of emotion recognition, a feature learning and classification algorithm based on convolutional neural network with 3D input is proposed. The single entropy（approximate entropy, permutation entropy and singular value decomposing entropy） and its combined entropy characteristics are used to perform emotion recognition experiments in the DEAP dataset on the two dimensions of valenceand arousal. The experimental results show that the accuracy of the combined entropy feature is significantly higher than that of the single entropy feature in the emotion recognition experiments. The average accuracy of the highest combined entropy characteristics are 94.14% and 94.44% in the valence and arousal, respectively, which are 5.05 percentage points and 4.49 percentage points higher than the average accuracy of the highest single entropy.