Emotion is the external expression of affect, which has an influence on cognition, perception, and decision-making of people’s daily life. As one of the basic problems in the realization of overall computer intelligence, emotion recognition has been studied in depth and widely applied in fields of affective computing and human-computer interaction. Comparing with facial expression, speech and other physiological signals, using EEG to recognize emotion is attracting more attention for its higher temporal resolution, lower cost, better identification accuracy, and higher reliability. In recent years, more deep learning architectures are applied and have achieved better performance than traditional machine learning methods in this task. Deep learning for EEG-based emotion recognition is one of the research focuses and it remains many challenges to overcome. Considering that there exist few reviews literature to refer to, this study investigates the implementation of deep learning in EEG-based emotion recognition. Specifically, input formulation, deep learning architecture, experimental setting and results are surveyed. Besides, articles that evaluated their model on the widely used datasets, DEAP and SEED, perform qualitative and quantitative analysis are carefully screened from different aspects and a comparison is accomplished. Finally, the total work is summarized and the prospect of future work is given.