• Can Deng, Jiasen Tong, Xuan Deng, Zhiyong Zhang, and Yurong Qin.
• School of Computer and Electronics Information, Guangxi University, China.
• Neuroscience. 2020 May 10; 434: 191-199.

AbstractEmotion recognition reflects the psychological and physiological status of humans. Numerous studies have investigated the neural mechanisms of emotion recognition based on electroencephalography (EEG) features. In the previous study, emotion target was presented under a static or irregular background, which made the response highly time-locked. As an oscillatory component of EEG, steady-state visual evoked potential (SSVEP) has distinctive frequency and phase properties, which provides more stable information than the other components of EEG. This study combined the emotion target with SSVEP to explore neural mechanisms of visual neurons under flickering background. Three basic emotions (delightfulness, sadness and, anger) were presented in 216 frequency-intensity conditions. Participants were asked to recognize the emotions and make judgments. The degree of alpha entrainment (valued as normalized Shannon entropy), SSVEP amplitude and recognition accuracy were calculated as response features. The results indicated that: SSVEP amplitude and recognition accuracy positively correlated with each other in frequency domain (7-15 Hz); alpha entrainment, and recognition accuracy had similar linear variation in intensity domain (level 1-4), and had a threshold around intensity 3; the three basic emotions had no clear relationship with each other in recognition. This study provided a new sight for neuroscience and would be an important reference to clinical psychology.Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

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