Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder - An, Yanling; Hu, Shaohai; Duan, Xiaoying; Zhao, Ling; Xie, Caiyun; Zhao, Yingying (Frontiers in Computational Neuroscience, 2021)
As one of the key technologies of emotion computing, emotion recognition has received great attention. EEG signals are spontaneous and difficult to camouflage, so it is used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). Firstly, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the three-dimensional features of EEG signals, which retains the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, a large number of experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions is 89.49% and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.