Brain Effective Connectivity Analysis from EEG for Positive and Negative Emotion

被引:17
作者
Zhang, Jianhai [1 ]
Zhao, Shaokai [1 ]
Huang, Wenhao [1 ]
Hu, Sanqing [1 ]
机构
[1] Hangzhou Dianzi Univ, Coll Comp Sci, Hangzhou 310018, Peoples R China
来源
NEURAL INFORMATION PROCESSING (ICONIP 2017), PT IV | 2017年 / 10637卷
基金
中国国家自然科学基金;
关键词
Emotion processing; Granger causality; Brain effective network; EEG; MUSIC;
D O I
10.1007/978-3-319-70093-9_90
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, there have been increasing evidence which supports that multiple brain regions are involved in emotion processing. Therefore, research on emotion from the perspective of brain network is becoming popular. In this study, based on the Granger causal analysis method, we constructed brain effective connectivity network from DEAP emotional EEG data to investigate how emotion affects the patterns of effective connectivity. According to our results, prefrontal region plays the most important role in emotion processing with interactions to almost all other regions. More interactions are found under negative emotion than positive one. Parietal region in charge of human's alert mechanism is more active under negative emotions. These results are consistent with the previous findings obtained in neuroscience, which illustrate the effectiveness of our methods. Furthermore, the brain effective connectivity network shows significant differences to different emotional states, so it can be used to recognize different emotional states with EEG.
引用
收藏
页码:851 / 857
页数:7
相关论文
共 17 条
[1]   NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].
AKAIKE, H .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) :716-723
[2]  
Aksop C., 2010, PAPERS, V5, P1049, DOI DOI 10.1016/0304-4076(92)90104-Y
[3]  
Bono V., 2016, IEEE INT C BIOM HLTH
[4]   Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment [J].
Ding, MZ ;
Bressler, SL ;
Yang, WM ;
Liang, HL .
BIOLOGICAL CYBERNETICS, 2000, 83 (01) :35-45
[5]  
Durbin J., 1992, Breakthroughs in Statistics, P237, DOI DOI 10.1007/978-1-4612-4380-9_20
[6]  
Friedman D, 2015, INT CONF AFFECT, P930, DOI 10.1109/ACII.2015.7344686
[7]   Mapping discrete and dimensional emotions onto the brain: controversies and consensus [J].
Hamann, Stephan .
TRENDS IN COGNITIVE SCIENCES, 2012, 16 (09) :458-466
[8]  
Heller W., 1993, Neuropsychology, V7, P476, DOI [10.1037/0894-4105.7.4.476, DOI 10.1037/0894-4105.7.4.476]
[9]  
James D.H., 2007, TIME SERIES ANAL
[10]   A Review on the Computational Methods for Emotional State Estimation from the Human EEG [J].
Kim, Min-Ki ;
Kim, Miyoung ;
Oh, Eunmi ;
Kim, Sung-Phil .
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2013, 2013