基于有向网络的人物信息诱发脑电信号特征
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摘要
基于熟人和陌生人的视听觉信息,通过记录对应的脑电信号,对大脑在熟人和陌生人信息刺激下的认知机制展开研究.首先,通过记录被试在视听刺激下的脑电信号,得到对应不同刺激下的事件相关脑电位.通过计算不同导联间的相位传递熵构建有向功能网络,最后对重点网络参数进行分析.结果表明,相比陌生人信息诱发的有向网络,熟人信息诱发网络中关键节点的作用加强,网络聚集能力增强;熟人信息诱发网络的连接更加趋向于全脑化,不同脑区间的信息交换加强,整个网络结构更有利于完成对熟人信息的识别.
Based on the visuo-auditory information of acquaintances and strangers,the possible differences of the cognitive mechanisms w ere studied by recording EEG signals. Firstly,the ERP signals for different stimuli types w ere obtained by recording the EEG signals of visuo-auditory stimuli. Then,the directed functional netw ork w as constructed by calculating the phase transfer entropy. Finally,netw ork parameters for the key connection w ere analyzed. The results show that compared w ith the directed netw ork induced by unfamiliar information,the role of key nodes in the netw ork of familiar information is strengthened,as w ell as the aggregation ability. The connections of the familiar netw ork tend to be more global,and the information exchange betw een different brain regions are increased,w hich is good for the identification of the familiars.
Based on the visuo-auditory information of acquaintances and strangers,the possible differences of the cognitive mechanisms w ere studied by recording EEG signals. Firstly,the ERP signals for different stimuli types w ere obtained by recording the EEG signals of visuo-auditory stimuli. Then,the directed functional netw ork w as constructed by calculating the phase transfer entropy. Finally,netw ork parameters for the key connection w ere analyzed. The results show that compared w ith the directed netw ork induced by unfamiliar information,the role of key nodes in the netw ork of familiar information is strengthened,as w ell as the aggregation ability. The connections of the familiar netw ork tend to be more global,and the information exchange betw een different brain regions are increased,w hich is good for the identification of the familiars.
引文
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[5] Sun D,Chang C,Lee T M. Identification and classification of facial familiarity in directed lying:an ERP study[J]. PloS One,2012,7(2):31250.
[6] Rodriguez Y S,Fuster J I,Leon M A B. Covert and overt face processing in healthy subjects:an ERP study[J].International Journal of Bioelectromagnetism,2008,10(4):246-260.
[7] Tanaka J W,Curran T,Porterfiled A L,et al. Activation of preexisting and acquired face representations:the N250 eventrelated potential as an index of face familiarity[J]. Journal of Cognitive Neuroscience,2006,18(9):1488-1497.
[8]常文文,王宏.基于听觉ERP功能脑网络特征和SVM的测谎方法研究[J].电子学报,2016,44(7):1757-1762.(Chang Wen-wen,Wang Hong. Study on lie detection method based on auditory ERP functional brain network characteristic and SVM[J]. Acta Electronica Sinica,2016,44(7):1757-1762.)
[9]高军峰,王沛,郑崇勋.基于P300和机器学习的测谎方法研究[J].西安交通大学学报,2010,44(10):120-124.(Gao Jun-feng,Wang Pei,Zheng Chong-xun. Lie detection method based on P300 and machine learning[J]. Journal of Xi’an Jiaotong University,2010,44(10):120-124.)
[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.(Liang Xia,Wang Jin-hui,He Yong. Human connectome:structure and functional brain networks[J]. Chinese Science Bulletin,2010,55(16):1565-1583.)
[11] van Diessen E,Numan T,van Dellen E,et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research[J]. Clinical Neurophysiology,2015,126(8):1468-1481.
[12] Lobier M,Siebenhuhner F,Palva S,et al. Phase transfer entropy:a novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions[J].NeuroImage,2014,85(2):853-872.
[13] Hesse W,Moller E,Arnold M,et al. The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies[J]. Journal of Neuroscience Methods,2003,124(1):27-44.
[14] Afshari S,Jalili M. Directed functional networks in alzheimer’ s disease:disruption of Glabal and local connectivity measuers[J]. IEEE Journal of Biomedical and Health Informatics,2016,21(4):949-955.
[15] Rubinov M,Sporns O. Complex network measures of brain connectivity:uses and interpretations[J]. NeuroImage,2009,52(3):1059-1069.
[16] Huang D,Ren A,Shang J,et al. Combining partial directed coherence and graph theory to analyse effective brain netw orks of different mental tasks[J]. Frontiers in Human Neuroscience,2016,10(5):1-11.
[17]黄璐,王宏.基于约束独立分量分析的脑电特征提取[J].东北大学学报(自然科学版),2014,35(3):419-423.(Huang Lu,Wang Hong. EEG feature extraction based on constrained ICA[J]. Journal of Northeastern University(Natural Science),2014,35(3):419-423.)