Sunday, January 17, 2016

Identifying Stable Patterns over Time for Emotion Recognition from EEG

Wei-Long Zheng, Jia-Yi Zhu, and Bao-Liang Lu, from Shanghai Jiao Tong University, recently published the paper "Identifying Stable Patterns over Time for Emotion Recognition from EEG" using a machine learning approach.


(...), we focus on identifying EEG stability in emotion recognition. (...) 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.

The full paper is available here.


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