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Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video-based external emotion evoking (i.e., rich media information), video-triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion-related studies in a laboratory environment, which provides constructive technical supports for establishing real-time emotion interaction systems. In this paper, we will focus on video-triggered EEG-based emotion recognition and present a systematical introduction of the current available video-triggered EEG-based emotion databases with the corresponding analysis methods. First, current video-triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB-HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video-triggered EEG-based emotion recognition will be systematically summarized and a brief review of current situation about video-triggered EEG-based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video-triggered EEG-emotion databases will be fully discussed.


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Video-triggered EEG-emotion public databases and current methods: A survey

Show Author's information Wanrou Hu1,2Gan Huang1,2Linling Li1,2Li Zhang1,2Zhiguo Zhang1,2,3Zhen Liang1,2( )
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong, China
Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, Guangdong, China
Peng Cheng Laboratory, Shenzhen 518055, Guangdong, China

Abstract

Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video-based external emotion evoking (i.e., rich media information), video-triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion-related studies in a laboratory environment, which provides constructive technical supports for establishing real-time emotion interaction systems. In this paper, we will focus on video-triggered EEG-based emotion recognition and present a systematical introduction of the current available video-triggered EEG-based emotion databases with the corresponding analysis methods. First, current video-triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB-HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video-triggered EEG-based emotion recognition will be systematically summarized and a brief review of current situation about video-triggered EEG-based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video-triggered EEG-emotion databases will be fully discussed.

Keywords:

emotion recognition, EEG signals, video-triggered, emotion database
Received: 18 August 2020 Revised: 06 September 2020 Accepted: 22 September 2020 Published: 04 February 2021 Issue date: September 2020
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Received: 18 August 2020
Revised: 06 September 2020
Accepted: 22 September 2020
Published: 04 February 2021
Issue date: September 2020

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