AUTHOR=Cao Rui , Hao Yan , Wang Xin , Gao Yuan , Shi Huiyu , Huo Shoujun , Wang Bin , Guo Hao , Xiang Jie 

TITLE=EEG Functional Connectivity Underlying Emotional Valance and Arousal Using Minimum Spanning Trees

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00355

DOI=10.3389/fnins.2020.00355

ISSN=1662-453X

ABSTRACT=In recent years, traditional methods such as power spectrum and amplitude analysis have been used to research on the emotional electroencephalogram (EEG). Brain network method is also used in emotional EEG research, which can better reflect the activity of brain. A minimum spanning tree (MST) represents the key information flow in the weighted brain network, and it provides an sensitive method to capture subtle informations in network organization while effectively avoiding the shortcomings of traditional brain networks. The DEAP dataset provides electroencephalogram (EEG) data for four categories of emotions: high arousal and high valence (HAHV), high arousal and low valence (HALV), low arousal and high valence (LAHV), and low arousal and low valence (LALV). Phase lag index (PLI) weighted matrices were calculated in five frequency bands. On this basis, the minimum spanning trees were constructed. At the same valence level in the gamma (γ) band, HAHV and HALV showed significant higher mean PLI (MPLI), maximum degree (Degreemax) and leaf fraction and significant lower diameter and eccentricity than LAHV and LALV. At the same arousal level in the γ band, HALV showed significant higher MPLI, Degreemax and leaf fraction and significant lower diameter and eccentricity than HAHV. These results indicate that the low-arousal showed more line-shaped configurations than the high-arousal. Additionally, in the high-arousal condition, a shift toward more star-shaped trees from high-valence to low-valence  supports the increase toward randomness of brain network with negative emotions and the brain is more activated when faced with negative emotions. From a brain network perspective, this phenomenon provides a theoretical basis for negative bias.