Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

Frontiers in Computational Neuroscience

Ling Zhao²  Yanling An³  Shaohai Hu³  Caiyun Xie⁴  Yingying Zhao⁴  Xiaoying Duan⁵ 

[1] College of Electronic and Information Engineering, Hebei University, Baoding, China;College of Quality and Technical Supervision, Hebei University, Baoding, China;Institute of Information Science, Beijing Jiaotong University, Beijing, China;Machine Vision Technology Creation Center of Hebei Province, Baoding, China;School of Economics and Management, Northwest University, Xi’an, China;

关键词: emotion recognition;    differential entropy;    feature fusion;    convolution neural network;    stacked autoencoder;   

DOI  :  10.3389/fncom.2021.743426

来源: DOAJ

【 摘 要 】

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are 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 a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain 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, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

【 授权许可】

Unknown