Research of Effective Network of Emotion Electroencephalogram Based on Sparse Bayesian Network_Journal of Biomedical Engineering

Authors：

GAOJia ,  WANGWei

School of Education Science, Nanjing Normal University, Nanjing 210097, China;

Corresponding author：

WANGWei, Email: wangwei5@njnu.edu.cn

Keywords：

sparse Bayesian network; emotion; theta; alpha; effective network

DOI：

10.7507/1001-5515.20150169

Video：

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Abstract Full text Figures/Tables Video References Cited by

Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: ① compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.

Citation： GAOJia, WANGWei. Research of Effective Network of Emotion Electroencephalogram Based on Sparse Bayesian Network. Journal of Biomedical Engineering, 2015, 32(5): 945-951. doi: 10.7507/1001-5515.20150169 Copy

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12.	CUI Jie, XU Lei, BRESSLER S L, et al. BSMART:A MATLAB/C toolbox for analysis of multichannel neural time series[J]. Neural Networks, 2008, 21(8, SI):1094-1104.
13.	RUBINOV M, SPORNS O. Complex network measures of brain connectivity:uses and interpretations[J]. Neuroimage, 2010, 52(3):1059-1069.
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1. 李亚鹏,沈蓉,戴慧,等.探索大脑网络连接的几种方式——基于静息态功能磁共振数据[J].中国医学物理学杂志, 2013, 30(1):3898-3902.
2. 刘颖,赵卉,陶韵文,等.脑结构网络和脑功能网络[J].中国科技论文在线精品论文, 2013, 6(23):2216-2222.
3. 蒋涛.基于格兰杰因果关系的视觉额顶网络的研究[D].成都:电子科技大学, 2013.
4. HUANG Shuai, LI Jing, YE Jieping, et al. A sparse structure learning algorithm for Gaussian Bayesian network identification from high-dimensional data[J]. IEEE Trans Pattern Anal Mach Intell, 2013, 35(6):1328-1342.
5. 王宁,魏玲,李颖洁.基于格兰杰因果分析情绪认知过程中alpha脑电特性[J].生物医学工程学杂志, 2012, 29(6):1021-1026.
6. 聂聃,王晓韡,段若男,等.基于脑电的情绪识别研究综述[J].中国生物医学工程学报, 2012, 31(4):595-606.
7. OMIGIE D, DELLACHERIE D, HASBOUN D, et al. An intracranial EEG study of the neural dynamics of musical valence processing[J]. Cereb Cortex, 2014:doi:10.1093/cercor/bhu118.
8. 乐国安,董颖红.情绪的基本结构:争论,应用及其前瞻[J].南开学报:哲学社会科学版, 2013,(1):140-150.
9. 汪小帆,李翔,陈关荣.复杂网络理论及其应用[M].北京:清华大学出版社, 2006.
10. 梁夏,王金辉,贺永.人脑连接组研究:脑结构网络和脑功能网络[J].科学通报, 2010, 55(16):1565-1583.
11. KOELSTRA S, MUHL C, SOLEYMANI M, et al. DEAP:A database for emotion analysis using physiological signals[J]. IEEE Transactions on Affective Computing, 2012, 3(1):18-31.
12. CUI Jie, XU Lei, BRESSLER S L, et al. BSMART:A MATLAB/C toolbox for analysis of multichannel neural time series[J]. Neural Networks, 2008, 21(8, SI):1094-1104.
13. RUBINOV M, SPORNS O. Complex network measures of brain connectivity:uses and interpretations[J]. Neuroimage, 2010, 52(3):1059-1069.
14. STURM V E, YOKOYAMA J S, SEELEY W W, et al. Heightened emotional contagion in mild cognitive impairment and Alzheimer's disease is associated with temporal lobe degeneration[J]. Proc Natl Acad Sci U S A, 2013, 110(24):9944-9949.
15. SCHMIDT L A, TRAINOR L J. Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions[J]. Cogn Emot, 2001, 15(4):487-500.

Journal of Biomedical Engineering

Research of Effective Network of Emotion Electroencephalogram Based on Sparse Bayesian Network

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