Electroencephalography (EEG)-based emotion recognition has advanced the field in affective computing and has enabled applications in human-computer interactions. Despite significant progress has been made in decoding emotion using supervised machine-learning methods, few studies applied data-driven, unsupervised approaches to explore the underlying EEG dynamics during an emotion experiment and examine how such dynamics correlate with subjective reports of emotion. This study employs the adaptive mixture independent component analysis (AMICA), an unsupervised approach, to EEG data from the DEAP dataset where 32 subjects watched emotional videos. Empirical results showed that AMICA could learn distinct models that separated EEG date collected in the emotion experiment. The identified changes in EEG patterns were weakly-correlated with the four reported emotion scales, indicating the underlying EEG dynamics partially reflected the emotional activities as well as the emotion-irrelevant brain dynamics. Further, the correlations between EEG dynamics and individuals' subjective emotional ratings were significantly higher than those between the EEG and the average ratings from online raters. Finally, building an emotion-decoding model based on the EEG dynamics revealed a significantly better classification performance for valence ratings compared to arousal. This study demonstrated the use of AMICA in characterizing the EEG dynamics in emotion experiments and provided insight into the relationship between EEG and the reported emotional experiences. The unsupervised learning approach can be applied to studying emotion and other confounding factors such as emotion irrelevant EEG artifacts, thereby improving the performance of emotion decoding for EEG-based affective computing.