Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials.
To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception.
Key Laboratory of Cognitive Science of State Ethnic Affairs Commission and Laboratory of Membrane Ion Channels and Medicine, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.|Hubei Key Laboatory of Medical Information Analysis &Tumor Diagnosis and Treatment, Wuhan, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Jun-Feng
JF
Gao
School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China.
Yong
Y
Yang
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory of Oceanographic Big Data Mining &Application of Zhejiang Province, Department of Physics, Zhejiang Ocean University, Zhoushan, China.
Wen-Tao
WT
Huang
Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, China.|Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China.
Pan
P
Lin
Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, China.
Sheng
S
Ge
Hubei Key Laboatory of Medical Information Analysis &Tumor Diagnosis and Treatment, Wuhan, China.|Department of Breast Surgery, Hubei Cancer Hospital, Wuhan, China.
Hong-Mei
HM
Zheng
Key Laboratory of Cognitive Science of State Ethnic Affairs Commission and Laboratory of Membrane Ion Channels and Medicine, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.
Ling-Yun
LY
Gu
Key Laboratory of Cognitive Science of State Ethnic Affairs Commission and Laboratory of Membrane Ion Channels and Medicine, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.
Hui
H
Zhou
Key Laboratory of Cognitive Science of State Ethnic Affairs Commission and Laboratory of Membrane Ion Channels and Medicine, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.|Hubei Key Laboatory of Medical Information Analysis &Tumor Diagnosis and Treatment, Wuhan, China.
Chen-Hong
CH
Li
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Ni-Ni
NN
Rao
D000328:Adult; D001921:Brain / Q000502:physiology*; D058256:Brain Waves; D008192:Deception; D004569:Electroencephalography; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D008014:Lie Detection; D008297:Male; D009434:Neural Pathways / Q000502:physiology; D058067:Wavelet Analysis; D055815:Young Adult