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

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.