Abstract: Affective computing (AC) is a new field of emotion research along with the development of computing technology and human-machine interaction technology. Emotion recognition is a crucial part of the AC research framework. Emotion recognition based on physiological signals provides richer information without deception than other techniques such as facial expression, tone of voice, and gestures. Many studies of emotion recognition have been conducted, but the classification accuracy is diverse due to variability in stimuli, emotion categories, devices, feature extraction and machine learning algorithms. This paper reviews all works that cited DEAP dataset (a public available dataset which uses music video to induce emotion and record EEG and peripheral physiological signals) and introduces detailed methods and algorithms on feature extraction, normalization, dimension reduction, emotion classification, and cross validation. Eventually, this work presents the application of AC on game development, multimedia production, interactive experience, and social network as well as the current limitations and the direction of future investigation.