Identifying Stable Patterns over Time for Emotion Recognition from EEG

Publication/Creation Date
January 10 2016
Wei-Long Zheng (creator)
Jia-Yi Zhu (creator)
Bao-Liang Lu (creator)
Shanghai Jiao Tong University (contributor)
Media Type
Journal Article
Persuasive Intent
Abstract—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.
HCI Platform
Discursive Type
Location on Body
Thinking, Emoting

Date archived
January 25 2016
Last edited
November 10 2020
How to cite this entry
Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu. (January 10 2016). "Identifying Stable Patterns over Time for Emotion Recognition from EEG". IEEE Transactions on Affective Computing. IEEE. Fabric of Digital Life.