Abstract
This paper presents a method to identify lying automatically using EEG signals. The wavelet entropy of event-related potentials (ERP) carries information about the degree of order associated with a multi-frequency brain electrophysiological activity. We used wavelet entropy to analyze ERP during a lying task. Ten subjects were divided into guilty and innocent groups randomly. They were instructed to make a truthful or deceptive responses on the stimuli. EEG recordings on Pz channel were collected and the features of wavelet entropy were extracted. Statistical result reveals that there is significantly lower wavelet entropy value for the guilty group than that for the control group. We concluded that guilty subjects showed much high order degree of the brain state than normal persons after about 300 ms after stimulus onset. Hence, wavelet entropy is an effective and reliable approach to detect deception, and can help us to understand cognition processing deeply for lying behaviors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Spence, S.A., Farrow, T.F., Herford, A.E., Wilkinson, I.D., Zheng, Y., Woodruff, P.W.: Behavioural and functional anatomical correlates of deception in humans. NeuroReport 12, 2849–2853 (2001). https://doi.org/10.1097/00001756-200109170-00019
Rosenfeld, J.P., Ellwanger, J.W., Nolan, K., et al.: P300 scalp amplitude distribution as an index of deception in a simulated cognitive deficit model. Int. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol. 33, 3–19 (1999). https://doi.org/10.1016/S0167-8760(99)00021-5
Allen, J.J.B., Iacono, W.G.: A comparison of methods for the analysis of event-related potentials in deception detection. Psychophysiology 34, 234–240 (1997). https://doi.org/10.1111/j.1469-8986.1997.tb02137.x
Abdulmajeed, A., Alexia, Z., Marco, F., et al.: A new method for detecting deception in event related potentials using individual-specific weight templates. BMC Neurosci. 14, 34 (2013). https://doi.org/10.1186/1471-2202-14-S1-P34
Johnson, M.M., Rosenfeld, J.P.: Oddball-evoked P300-based method of deception detection in the laboratory. II: utilization of non-selective activation of relevant knowledge. Int. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol. 12, 289–306 (1992). https://doi.org/10.1016/0167-8760(92)90067-L
Elaad, E.: Effects of context and state of guilt on the detection of concealed crime information. Int. J. Psychophysiol. 71, 225–234 (2009). https://doi.org/10.1016/j.ijpsycho.2008.10.001
Verschuere, B., Spruyt, A., Meijer, E.H., et al.: The ease of lying. Conscious. Cognit. 20, 908–911 (2011). https://doi.org/10.1016/j.concog.2010.10.023
Sun, D., Lee, T.M.C., Chan, C.C.H.: Unfolding the spatial and temporal neural processing of lying about face familiarity. Cereb. Cortex 25, 927–936 (2015). https://doi.org/10.1093/cercor/bht284
Quiroga, R.Q., Rosso, O.A., Başar, E., et al.: Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations. Biol. Cybern. 84, 291–299 (2001). https://doi.org/10.1007/s004220000212
Abásolo, D., Hornero, R., Espino, P., et al.: Entropy analysis of the EEG background activity in Alzheimer’s disease patients. Physiol. Meas. 27, 241–253 (2006). https://doi.org/10.1088/0967-3334/27/3/003
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (2004)
Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5, 3–55 (2001)
Inouye, T., Shinosaki, K., Sakamoto, H., et al.: Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalogr. Clin. Neurophysiol. 79, 204–210 (1991). https://doi.org/10.1016/0013-4694(91)90138-T
Rosso, O.A., Yordanova, J., Kolev, V., et al.: Time-frequency analysis of sensorial brain activity. Suppl. Clin. Neurophysiol. 54, 443–450 (2002). https://doi.org/10.1016/s1567-424X(09)70485-4
Phan, K.L., Magalhaes, A., Ziemlewicz, T.J., et al.: Neural correlates of telling lies: a functional magnetic resonance imaging study at 4 Tesla. Acad. Radiol. 12, 164–172 (2005). https://doi.org/10.1016/j.acra.2004.11.023
Gao, J.F., Wang, Z., Yang, Y., et al.: A novel approach for lie detection based on F-score and extreme learning machine. PLoS ONE 8, e64704 (2013). https://doi.org/10.1371/journal.pone.0064704
Acknowledgment
The work was supported by the National Nature Science Foundation of China (No. 81271659 and 61773408), the China Postdoctoral Science Foundation (No. 2014M552346).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xiong, Y., Gao, J., Chen, R. (2018). Wavelet Entropy Analysis for Detecting Lying Using Event-Related Potentials. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_46
Download citation
DOI: https://doi.org/10.1007/978-981-13-0893-2_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0892-5
Online ISBN: 978-981-13-0893-2
eBook Packages: Computer ScienceComputer Science (R0)