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

Xiaohu Wang; Yongmei Ren; Ze Luo; Wei He; Jun Hong; Yinzhen Huang

doi:10.3389/fpsyg.2023.1126994

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

Front Psychol. 2023 Feb 27:14:1126994. doi: 10.3389/fpsyg.2023.1126994. eCollection 2023.

Authors

Xiaohu Wang¹, Yongmei Ren², Ze Luo¹, Wei He², Jun Hong¹, Yinzhen Huang³

Affiliations

¹ School of Intelligent Manufacturing and Mechanical Engineering, Hunan Institute of Technology, Hengyang, China.
² School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China.
³ School of Computer and Information Engineering, Hunan Institute of Technology, Hengyang, China.

Abstract

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.

Keywords: deep learning; electroencephalogram; emotion recognition; human–computer interaction; survey.

Publication types

Review