Deep learning-based EEG emotion recognition: Current trends and future perspectives

Frontiers in Psychology

Psychology

Yinzhen Huang¹  Yongmei Ren²  Wei He²  Xiaohu Wang³  Jun Hong³  Ze Luo³ 

[1] School of Computer and Information Engineering, Hunan Institute of Technology, Hengyang, China;School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China;School of Intelligent Manufacturing and Mechanical Engineering, Hunan Institute of Technology, Hengyang, China;

关键词: human–computer interaction;    electroencephalogram;    emotion recognition;    deep learning;    survey;   

DOI  :  10.3389/fpsyg.2023.1126994

 received in 2022-12-19, accepted in 2023-01-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human–computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Ren, Luo, He, Hong and Huang.