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EEG/PPG effective connectivity fusion for analyzing deception in interview

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Abstract

In this research, the interaction between electroencephalogram (EEG) and, a cardiac parameter, photoplethysmogram (PPG), using connectivity measures to emphasize the importance of autonomic nervous system over the central nervous system during a deception is investigated. In this survey, connectivity analysis was applied, since it can provide information flow of brain regions; moreover, lying and truth appear to be cohered with the flow of information in the brain. Initially, a new wavelet-based approach for EEG/PPG effective connectivity fusion was introduced; then, it was validated for 41 subjects. For each subject, after extracting specific wavelet component of EEG and PPG signals, an effective connectivity network was generated by a generalized partial direct coherence and a direct directed transfer function. The results showed that grand average connectivity patterns were different in some regions for guilty and innocent subjects. The classification results demonstrated that lying could be discriminated from truth with the average accuracy of 84.14% by the leave-one-subject-out method. The present results contribute new information about coupling between EEG and PPG signals.

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Notes

  1. Guilty Knowledge Test.

  2. Concealed Information Test.

References

  1. Matte, J.: Forensic Psychophysiology Using the Polygraph: Scientific Truth Verification, Lie Detection (1996)

  2. Shi, Y., Nguyen, M.H., Blitz, P., French, B., Fisk, S., De la Torre, F., Smailagic, A., Siewiorek, D.P., al’ Absi, M., Ertin, E., Kamarck, T., Kumar, S.: Personalized stress detection from physiological measurements. In: International Symposium on Quality of Life Technology (2010)

  3. Karthikeyan, P., Murugappan, M., Yaacob, S.: A review on stress inducement stimuli for assessing human stress using physiological signals. In: 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (CSPA). IEEE (2011)

  4. Rajoub, B.A., et al.: Thermal facial analysis for deception detection. IEEE Trans. Inf. Forensics Secur. 9(6), 1015–1023 (2014). https://doi.org/10.1109/tifs.2014.2317309

    Article  Google Scholar 

  5. Warmelink, L., Vrij, A., Mann, S., Leal, S., Forrester, D., Fisher, R.P.: Thermal imaging as a lie detection tool at airports. Law Hum. Behav. 35(1), 40–48 (2011)

    Article  Google Scholar 

  6. Abootalebi, V., Moradi, M.H., Khalilzadeh, M.A.: A new approach for EEG feature extraction in P300-based lie detection. Comput. Methods Progr. Biomed. 94, 48–57 (2009). https://doi.org/10.1016/j.cmpb.2008.10.001

    Article  Google Scholar 

  7. Mehrnam, A.H., Nasrabadi, A.M., et al.: A new approach to analyze data from EEG-based concealed face recognition system. Int. J. Psychophysiol. 116, 1–8 (2017). https://doi.org/10.1016/j.ijpsycho.2017.02.005

    Article  Google Scholar 

  8. Ghodousi, M., Nasrabadi, A.M., et al.: Combination of event related potentials and peripheral signals in order to improve the accuracy of the lie detection systems. JSDP 12(2), 73–86 (2015)

    Google Scholar 

  9. Raskin, D.C., Honts, C.R., Kircher, J.C. (eds.): Credibility Assessment. Scientific Research and Applications. Academic Press, Cambridge (2014)

    Google Scholar 

  10. Davatzikos, C., Ruparel, K., Fan, Y., Shen, D.G., Acharyya, M., Loughead, J.W., et al.: Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Neuroimage 28, 663–668 (2005)

    Article  Google Scholar 

  11. Gao, J., et al.: Exploring time and frequency dependent functional connectivity and brain networks during deception with single-trial event-related potentials. Sci. Rep. 6, 37065 (2016). https://doi.org/10.1038/srep37065

    Article  Google Scholar 

  12. Wang, H., Chang, W., Zhang, C.: Functional brain network and multichannel analysis for the P300-based Brain Computer Interface system of lying detection. Expert Syst. Appl. (2016). https://doi.org/10.1016/j.eswa.2016.01.024

    Article  Google Scholar 

  13. Wang, Y., Ng, W.C., Ng, K.S., Yu, K., Wu, T., Li, X.: An Electroencephalography network and connectivity analysis for deception in instructed lying tasks. PLoS ONE 10(2), e0116522 (2015). https://doi.org/10.1371/journal.pone.0116522

    Article  Google Scholar 

  14. Daneshi Kohan, M, et al.: Interview based connectivity analysis of EEG in order to detect deception. Med. Hypotheses 136 (2020). https://doi.org/10.1016/j.mehy.2019.109517

  15. Passaro, A.D., et al.: A novel method linking neural connectivity to behavioral fluctuations: behavior-regressed connectivity. J. Neurosci. Methods 279, 60–71 (2017)

    Article  Google Scholar 

  16. Chang, C., et al.: Association between heart rate variability and fluctuations in resting-state functional connectivity. Neuroimage 68, 93–104 (2013). https://doi.org/10.1016/j.neuroimage.2012.11.038

    Article  Google Scholar 

  17. Jurysta, F., et al.: A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men. Clin. Neurophysiol. 114, 2146–2155 (2003)

    Article  Google Scholar 

  18. Kim, D.-K., et al.: Dynamic correlations between heart and brain rhythm during autogenic meditation. Front. Hum. Neurosci. 7, 414 (2013). https://doi.org/10.3389/fnhum.2013.00414

    Article  Google Scholar 

  19. Piper, D., et al.: Time-variant coherence between heart rate variability and EEG activity in epileptic patients: an advanced coupling analysis between physiological networks. New J. Phys. 16, 115012 (2014). https://doi.org/10.1088/1367-2630/16/11/115012

    Article  Google Scholar 

  20. Mazzillo, M., et al.: Characterization of SiPMs with NIR long-pass Interferential and plastic filters. IEEE Photonics J. 10(3), 1–12 (2018). https://doi.org/10.1109/jphot.2018.2834738

    Article  Google Scholar 

  21. Rundo, F., et al.: A nonlinear pattern recognition Pipeline for PPG/ECG medical assessments. In: Springer Nature, Proceedings of the Fourth National Conference on Sensors, 21–23 February 2018, Catania, Italy. http://doi.org/10.1007/978-3-030-04324-7_57 (2018)

  22. Vincenzo, et al.: PPG/ECG multisite Combo system based on SiPM Technology. Sensors 353–360, 2017 (2018). https://doi.org/10.1007/978-3-030-04324-7_44

    Article  Google Scholar 

  23. Anastasova, K., et al.: Differences in the quality of the photoplethysmograph signal in subjects with and without nail polish. Eur. Polygraph 12(1–43), 7–17 (2018). https://doi.org/10.2478/ep-2018-0001

    Article  Google Scholar 

  24. Mullen, T.R., Kothe, C.A., Chi, Y.M., Ojeda, A., Kerth, T., Makeig, S., Jung, T.P., Cauwenberghs, G.: Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Biomed. Eng. 62(11), 2553–2567 (2015). https://doi.org/10.1109/tbme.2015.2481482

    Article  Google Scholar 

  25. Volz, K.G., Schubotz, R.I., Yves von Cramon, D.: Decision making and the frontal lobes. Curr. Opin. Neurol. 19(4), 401–406 (2006). https://doi.org/10.1097/01.wco.0000236621.83872.71

    Article  Google Scholar 

  26. Collins, A., Koechlin, E.: Reasoning, learning, and creativity: frontal lobe function and human decision-making. PLOS Biol. 10(3), e1001293 (2012). https://doi.org/10.1371/journal.pbio.1001293

    Article  Google Scholar 

  27. Peplow, M.: Brain imaging could spot liars. Nature (2004). https://doi.org/10.1038/news041129-1

    Article  Google Scholar 

  28. Langleben, D.D., et al.: Brain activity during simulated deception: an event-related functional magnetic resonance study. NeuroImage 15(3), 727–732 (2002)

    Article  Google Scholar 

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Acknowledgements

I would like to acknowledge National Brain Mapping Laboratory of Iran to provide us with their standard EEG recording system and helping us to collect data.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Marzieh Daneshi Kohan

  2. Department of Biomedical Engineering, Shahed University, Tehran, Iran

    Ali Motie Nasrabadi

  3. Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran

    Mohammad Bagher Shamsollahi

  4. Department of Signal Processing, Research Center for Development of Advanced Technologies, Tehran, Iran

    Ali Sharifi

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  1. Marzieh Daneshi Kohan
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  2. Ali Motie Nasrabadi
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Correspondence to Ali Motie Nasrabadi.

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Daneshi Kohan, M., Motie Nasrabadi, A., Shamsollahi, M.B. et al. EEG/PPG effective connectivity fusion for analyzing deception in interview. SIViP 14, 907–914 (2020). https://doi.org/10.1007/s11760-019-01622-1

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  • DOI: https://doi.org/10.1007/s11760-019-01622-1

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