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.

Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence-based detections/classifications. Around 10 million transistors are integrated into a 1 mm² silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog-digital converters on the silicon surface makes it possible to place the analog front-end circuits within a tiny packaged chip; therefore, enabling high-count EEG acquisition channels. This article introduces and reviews the state-of-the-art dedicated chip designs for EEG processing, particularly for wearable systems. Furthermore, the analog circuits and digital platforms are included, and the technical details of circuit topology and logic architecture are presented in detail.