  In this paper, we investigate stable patterns of electroencephalogram (EEG)
over time for emotion recognition using a machine learning approach. Up to now,
various findings of activated patterns associated with different emotions have
been reported. However, their stability over time has not been fully
investigated yet. In this paper, we focus on identifying EEG stability in
emotion recognition. To validate the efficiency of the machine learning
algorithms used in this study, we systematically evaluate the performance of
various popular feature extraction, feature selection, feature smoothing and
pattern classification methods with the DEAP dataset and a newly developed
dataset for this study. The experimental results indicate that stable patterns
exhibit consistency across sessions; the lateral temporal areas activate more
for positive emotion than negative one in beta and gamma bands; the neural
patterns of neutral emotion have higher alpha responses at parietal and
occipital sites; and for negative emotion, the neural patterns have significant
higher delta responses at parietal and occipital sites and higher gamma
responses at prefrontal sites. The performance of our emotion recognition
system shows that the neural patterns are relatively stable within and between
sessions.
