基于互信息的脑网络及测谎研究

doi:10.3969/j.issn.0372-2112.2019.07.021

电子学报 ›› 2019, Vol. 47 ›› Issue (7): 1551-1556.DOI: 10.3969/j.issn.0372-2112.2019.07.021

基于互信息的脑网络及测谎研究

彭丝雨, 周到, 张家琦, 王宇, 高军峰

中南民族大学生物医学工程学院认知科学国家民委重点实验室, 湖北武汉 430074

收稿日期:2018-10-15 修回日期:2019-03-05 出版日期:2019-07-25
- 通讯作者:
- 高军峰
- 作者简介:
- 彭丝雨 女,1994年5月出生,安徽合肥人.2017年毕业于上海理工大学,现为中南民族大学国家民委认知科学重点实验室研究生,研究方向为脑电信号处理与脑认知、功能研究分析;周到女,1983年3月出生,湖北武汉人.讲师.2007年和2010在华中科技大学获得工学硕士和工学博士学位,现为中南民族大学教师,主要从事生物医学信号处理、脑信号解析和模式识别等方面的研究工作.
- 基金资助:
- 国家自然科学基金 (No.81271659，No.61773408）; 中央高校基本科研业务费专项资金 (No.CZZ19004）

Research on Mutual Information-Based Brain Network and Lie Detection

PENG Si-yu, ZHOU Dao, ZHANG Jia-qi, WANG Yu, GAO Jun-feng

Key Laboratory of Cognitive Science, State Ethnic Affairs Commission, School of Biomedical Engineering, South-Central University for Nationalities, Wuhan, Hubei 430074, China

Received:2018-10-15 Revised:2019-03-05 Online:2019-07-25 Published:2019-07-25

摘要/Abstract

摘要： 互信息分析方法是基于信息论提出的一种描述两信号间信息交互情况的算法，其在脑电信号领域的有效性已得到了充分证实.针对当前测谎方法中脑电信号特征提取困难以及大脑整体认知功能分析在脑认知科学研究中越来越被重视的情况，本文首次将互信息分析方法应用到脑电测谎领域中，使用互信息量化大脑各节点之间的相关性，对计算结果进行统计分析，选取出在两类人群中具有显著性差异的电极对的互信息作为分类特征，进行模式识别，得到了99.67%的准确率.这一结果表明，互信息分析方法是一种有效的脑功能连接分析方法，为基于脑电信号连接分析的测谎研究提供了一种新的途径.另外，对说谎与诚实两类受试者的大脑功能网络的分析结果表明：处于说谎状态时，大脑的额叶、顶叶、颞叶及枕叶之间协同实现谎言功能，并在躯体行为所对应的脑区与其他脑区的连接上也表现出相对诚实组的显著性差异，以上结果均有助于进一步揭示谎言的神经活动机制.

关键词: 互信息, 脑电, 脑网络, 测谎, 特征提取, 功能连接

Abstract: The mutual information analysis is a method based on information theory to describe the information interaction between two signals.In view of the difficulty in extracting features of EEG signals in the current lie detection method and the circumstance that the analysis of the overall cognitive function of the brain were increasingly important in brain cognitive science research,this paper applied the mutual information analysis method to the field of EEG lie detection for the first time and quantified the correlation between the brain nodes and perform statistical analysis on the calculation results.The mutual information of the electrode pairs with significant differences in the two groups were selected as the classification features,on which the pattern recognition was performed,resulting in the accuracy rate of 99.67%.This result proves that the mutual information analysis is an effective brain functional connection analysis method,which provides a new way for lie detection research based on EEG signal connection analysis.In addition,the brain function network of both lying and honest subjects was also analyzed.The results show that when lying,the frontal,parietal,temporal,and occipital regions of the brain cooperate to achieve the lie function,and in the connection between the brain regions corresponding to the physical behavior and other brain regions,significant differences between the two groups was also shown.These above results will help us further reveal the neural activity mechanism of the lie.

Key words: mutual information, EEG, brain networks, lie detection, feature extraction, functional connectivity

中图分类号:

R338

彭丝雨, 周到, 张家琦, 王宇, 高军峰. 基于互信息的脑网络及测谎研究[J]. 电子学报, 2019, 47(7): 1551-1556.

PENG Si-yu, ZHOU Dao, ZHANG Jia-qi, WANG Yu, GAO Jun-feng. Research on Mutual Information-Based Brain Network and Lie Detection[J]. Acta Electronica Sinica, 2019, 47(7): 1551-1556.

参考文献

[1] STEDMON A W,et al.Scalable interrogation:Eliciting human pheromone responses to deception in a security interview setting[J].Applied Ergonomics,2015,47:26-33.
[2] MATSUDA I,OGAWA T.Improved method for calculating the respiratory line length in the concealed information test[J].International Journal of Psychophysiology,2011,81(2):65-71.
[3] SUTTON S,BRAREN M,ZUBIN J,et al.Evoked-potential correlates of stimulus uncertainty[J].Science,1965,150(3700):1187.
[4] ROSENFELD J P,NASMAN V T,WHALEN R,et al.Late vertex positivity in event-related potentials as a guilty knowledge indicator:a new method of lie detection[J].International Journal of Neuroscience,1987,34(1-2):125.
[5] ABOOTALEBI V,MORADI M H,KHALILZADEL M A.A new approach for EEG feature extraction in P300-based lie detection[J].Computer Methods & Programs in Biomedicine,2009,94(1):48-57.
[6] CHUN Y S,JI W J,JEON H,et al.Neural correlates of deception in lie detection using EEG frequency analysis[J].International Journal of Psychophysiology,2014,94(2):260-260.
[7] ITO A,ABE N,FUJⅡ T,et al.The role of the dorsolateral prefrontal cortex in deception when remembering neutral and emotional events[J].Neuroscience Research,2011,69(2):121-128.
[8] FARWELL L A,DONCHIN E.The truth will out:interrogative polygraphy (lie detection) with event-related brain potentials[J].Psychophysiology,2010,28(5):531-547.
[9] ROSENFELD J P,SOSKINS M,BOSH G,et al.Simple,effective countermeasures to P300-based tests of detection of concealed information[J].Psychophysiology,2010,41(2):205-219.
[10] GAO J,YAN X,SUN J,et al.Denoised P300 and machine learning-based concealed information test method[J].Computer Methods & Programs in Biomedicine,2011,104(3):410-417.
[11] GAO J,WANG Z,YANG Y,et al.A novel approach for lie detection based on F-score and extreme learning machine[J].Plos One,2013,8(6):e64704.
[12] 顾凌云,吕文志,杨勇,等.基于PCANet和SVM的谎言测试研究[J].电子学报,2016,44(8):1969-1973. GU L Y,LV W Z,YANG Y,et al.Deception detection study based on PCANet and support vector machine[J].Acta Electronica Sinica,2016,44(8):1969-1973.(in Chinese)
[13] 陈冉,王鹏,高军峰,等.利用相锁值算法的脑电相同步测谎研究[J].电子学报,2018,46(6):1289-1293. CHEN R,WANG P,GAO J F,et al.Lie detection study based on phase locking value[J].Acta Electronica Sinica,2018,46(6):1289-1293.(in Chinese)
[14] PINOTSIS D A,HANSEN E,FRISON K J,et al.Anatomical connectivity and the resting state activity of large cortical networks[J].Neuroimage,2013,65(4):127-138.
[15] 李兰兰.基于多导联EEG的精神分裂患者alpha波段连接性算法的研究[D].兰州:兰州大学,2013.
[16] YE X,TIAN T,XU T,et al.Analysis of alpha wave epileptic EEG signals based on symbolic transfer entropy[A].International Conference on Mechatronics,Robotics and Automation[C].Atlantis Press,2015.448-452.
[17] NESAEI S,SHARAFAT A R.Real-time mining of epileptic seizure precursors via nonlinear mapping and dissimilarity features[J].Signal Processing Iet,2015,9(3):193-200.
[18] GIGOLA S,ORTIZ F,D'Attellis C E,et al.Prediction of epileptic seizures using accumulated energy in a multiresolution framework[J].Journal of Neuroscience Methods,2004,138(1):107-111.
[19] JEONG J,GORE J C,PETERSON B S.Mutual information analysis of the EEG in patients with Alzheimer's disease[J].Clinical Neurophysiology,2001,112(5):827-835.
[20] LIU C J,HUANG C F,HUANG R Y,et al.Solving reality problems by using mutual information analysis[J].Mathematical Problems in Engineering,2014,2014(1):1-4.
[21] HUANG L Y,ZOU J,MA H J,et al.Brain functional network based on mutual information analysis of EEGs and its application to schizophrenia[J].Advanced Materials Research,2013,718-720:1440-1445.
[22] 李红利,王江,邓斌,等.癫痫脑电的互信息和同步性分析[J].计算机工程与应用,2013,49(6):19-22.
[23] 赵静.基于互信息的EEG脑功能网络研究[D].西安:西安电子科技大学,2013.
[24] FRASCHINI M,DEMURU M,CROBE A,et al.The effect of epoch length on estimated EEG functional connectivity and brain network organization[J].Journal of Neural Engineering,2016,13(3):036015.
[25] MCDOWELL K,KERICK S E,OIE K.Non-linear brain activity in real-world settings:movement artifact and the phase lag index[A].Army Science Conference[C].United States:US Army Research Laboratory,2010.309-316.
[26] POLAT K,GUNES S.A new feature selection method on classification of medical datasets:Kernel F-score feature selection[J].Expert Systems with Applications,2009,36(7):10367-10373.
[27] RIAZ Q,VOGELE A,KRUGER B,et al.One small step for a man:estimation of gender,age and height from recordings of one step by a single inertial sensor[J].Sensors,2015,15(12):31999-32019.
[28] WATTS D J,STROGATZ S H.Collective dynamics of ‘small-world’ networks[J].Nature,1998:440-442.
[29] LEISTEDT S J,COUMANS N,DUMONT M,et al.Altered sleep brain functional connectivity in acutely depressed patients[J].Human Brain Mapping,2009,30(7):2207-2219.
[30] 加扎尼加,伊夫里曼根,周晓林,等.认知神经科学:关于心智的生物学[M].中国轻工业出版社,2011.
[31] 孙俊峰,洪祥飞,童善保.复杂脑网络研究进展——结构、功能、计算与应用[J].复杂系统与复杂性科学,2010,07(4):74-90.

基于互信息的脑网络及测谎研究

Research on Mutual Information-Based Brain Network and Lie Detection

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