Skip to main content
SpringerLink
Log in

Classification of EEG signals using hybrid combination of features for lie detection

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The present work demonstrates the effectiveness of the combination of time, frequency, time–frequency, and statistical features extracted from the electroencephalogram (EEG) data, with support vector machine (SVM) for lie detection. Predominantly, the features extracted from the empirical mode decomposition (EMD) of the EEG data significantly improve the classification accuracy. A specific number of narrow band oscillatory components, called intrinsic mode functions (IMFs), are obtained after EMD of the data. The first three IMFs are selected to extract three time and three frequency domain statistical features corresponding to each IMF. These features are chosen due to the strong data adaptation capability of EMD for the transient signals such as an EEG. Furthermore, the features are selected keeping in mind the differences in the distribution, average value, and regularity of the guilty and innocent subjects’ brain signals. The proposed combination of extracted features with customized SVM demonstrates better accuracy than the other state-of-the-art feature extraction methods reported earlier. The proposed hybrid combination of features prominently distinguishes the guilty and innocent subjects with the classification accuracy of 99.44%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Matsuda I, Nittono H, Hirota A, Ogawa T, Takasawa N (2009) Event-related brain potentials during the standard autonomic-based concealed information test. Int J Psychophysiol 74:58–68

    Article  Google Scholar 

  2. Farahani ED, Moradi MH (2013) A concealed information test with combination of ERP recording and autonomic measurements. Neurophysiology 45:223–233

    Article  Google Scholar 

  3. Teplan M (2002) Fundamentals of EEG measurement. Meas Sci Rev 2:1–11

    Google Scholar 

  4. Jackson AF, Bolger DJ (2014) The neurophysiological bases of EEG and EEG measurement: a review for the rest of us. Psychophysiology 51:1061–1071

    Article  Google Scholar 

  5. Picton TW, Lins OG, Scherg M (1995) The recording and analysis of event-related potentials. In: Boller F, Grafman J (eds) Handbook of neuropsychology, vol 10. Elsevier, Amsterdam, pp 3–73

    Google Scholar 

  6. Lafuente V, Gorriz JM, Ramirez J, Gonzalez E (2017) P300 brainwave extraction from EEG signals: an unsupervised approach. Expert Syst Appl 74:1–10

    Article  Google Scholar 

  7. Polich J, Alexander JE, Bauer LO, Kuperman S, Morzorati S, O’Connor SJ, Porjesz B, Rohrbaugh J, Begleiter H (1997) P300 topography of amplitude/latency correlations. Brain Topogr 9:275–282

    Article  Google Scholar 

  8. González MA, Garduño E, Bribiesca E, Suárez OY, Bañuelos VM (2016) P300 detection based on EEG shape features. Comput Math Method M 2016:1–14

    Article  Google Scholar 

  9. Rosenfeld JP, Hu X, Pederson K (2012) Deception awareness improves P300-based deception detection in concealed information tests. Int J Psychophysiol 86:114–121

    Article  Google Scholar 

  10. Gao J, Tian H, Yang Y, Yu X, Li C, Rao N (2014) A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM. PLoS One. https://doi.org/10.1371/journal.pone.0109700

    Article  Google Scholar 

  11. Mehrnam AH, Nasrabadi AM, Ghodousi M, Mohammadian A, Torabi S (2017) A new approach to analyze data from EEG-based concealed face recognition system. Int J Psychophysiol 116:1–8

    Article  Google Scholar 

  12. Unser M, Aldroubi A (1996) A review of wavelets in biomedical applications. Proc IEEE 84:626–638

    Article  Google Scholar 

  13. Wang D, Miao D, Blohm G (2013) A new method for EEG-based concealed information test. IEEE Trans Inf Forensics Secur 8:520–527

    Article  Google Scholar 

  14. Arasteh A, Moradi MH, Janghorbani A (2016) A novel method based on empirical mode decomposition for P300-based detection of deception. IEEE Trans Inf Forensics Secur 11:2584–2593

    Article  Google Scholar 

  15. Garrett D, Peterson DA, Anderson CW, Thaut MH (2003) Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans Neural Syst Rehabil Eng 11:141–144

    Article  Google Scholar 

  16. Davatzikos C, Ruparel K, Fan Y, Shen D, Acharyya M, Loughead J, Gur R, Langleben DD (2005) Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 28:663–668

    Article  Google Scholar 

  17. Subasi A, Erçelebi E (2005) Classification of EEG signals using neural network and logistic regression. Comput Meth Prog Biomed 78:87–99

    Article  Google Scholar 

  18. Demiralp T, Yordanova J, Kolev V, Ademoglu A, Devrim M, Samar VJ (1999) Time-frequency analysis of single-sweep event-related potentials by means of fast wavelet transform. Brain Lang 66:129–145

    Article  Google Scholar 

  19. Samar VJ, Bopardikar A, Rao R, Swartz K (1999) Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain Lang 66:7–60

    Article  Google Scholar 

  20. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995

    Article  MathSciNet  Google Scholar 

  21. Oweis RJ, Abdulhay EW (2011) Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed Eng OnLine. https://doi.org/10.1186/1475-925X-10-38

    Article  Google Scholar 

  22. Hu X, Pornpattananangkul N, Rosenfeld JP (2013) N200 and P300 as orthogonal and integrable indicators of distinct awareness and recognition processes in memory detection. Psychophysiology 50:454–464

    Article  Google Scholar 

  23. Zhao M, Zheng C, Zhao C (2012) A new approach for concealed information identification based on ERP assessment. J Med Syst 36:2401–2409

    Article  Google Scholar 

  24. Gao J, Wang Z, Yang Y, Zhang W, Tao C, Guan J, Rao N (2013) A novel approach for lie detection based on F-score and extreme learning machine. PLoS One. https://doi.org/10.1371/journal.pone.0064704

    Article  Google Scholar 

  25. Abootalebi V, Moradi MH, Khalilzadeh MA (2009) A new approach for EEG feature extraction in P300-based lie detection. Comput Meth Prog Biomed 94:48–57

    Article  Google Scholar 

  26. Kalatzis I, Piliouras N, Ventouras E, Papageorgiou CC, Rabavilas AD, Cavouras D (2004) Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals. Comput Meth Prog Biomed 75:11–22

    Article  Google Scholar 

  27. Gao J, Yan X, Sun J, Zheng C (2011) Denoised P300 and machine learning-based concealed information test method. Comput Meth Prog Biomed 104:410–417

    Article  Google Scholar 

  28. Faust O, Acharya UR, Min LC, Sputh BHC (2010) Automatic identification of epileptic and background EEG signals using frequency domain parameters. Int J Neural Syst 20:159–176

    Article  Google Scholar 

  29. Bajaj V, Guo Y, Sengur A, Siuly S, Alcin OF (2017) A hybrid method based on time-frequency images for classification of alcohol and control EEG signals. Neural Comput Appl 28:3717–3723

    Article  Google Scholar 

  30. Abootalebi V, Moradi MH, Khalilzadeh MA (2006) A comparison of methods for ERP assessment in a P300-based GKT. Int J Psychophysiol 62:309–320

    Article  Google Scholar 

  31. Demiralp T, Ademoglu A, Comerchero M, Polich J (2001) Wavelet analysis of P3a and P3b. Brain Topogr 13:251–267

    Article  Google Scholar 

  32. Gao J, Lu L, Yang Y, Yu G, Na L, Rao N (2012) A novel concealed information test method based on independent component analysis and support vector machine. Clin EEG Neurosci 43:54–63

    Article  Google Scholar 

  33. Flandrin P (2007) Matlab/C codes for EMD and EEMD with examples. http://perso.ens-lyon.fr/patrick.flandrin/emd.html. Accessed 23 Aug 2017

  34. Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K (2016) EMD based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans Neural Syst Rehabil Eng 24:28–35

    Article  Google Scholar 

  35. Alam SMS, Bhuiyan MIH (2013) Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J Biomed Health Inform 17:312–318

    Article  Google Scholar 

  36. Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43:807–816

    Article  Google Scholar 

  37. Chen SW, Lai YC (2014) A signal-processing-based technique for P300 evoked potential detection with the applications into automated character recognition. EURASIP J Adv Sig Pr. https://doi.org/10.1186/1687-6180-2014-152

    Article  Google Scholar 

  38. Zhang Y, Ji X, Liu B, Huang D, Xie F, Zhang Y (2017) Combined feature extraction method for classification of EEG signals. Neural Comput Appl 28:3153–3161

    Article  Google Scholar 

  39. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  40. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual workshop on computational learning theory, Pittsburgh, Pennsylvania, USA, pp 144–152

  41. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167

    Article  Google Scholar 

  42. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM T Intel Syst Tech. https://doi.org/10.1145/1961189.1961199

    Article  Google Scholar 

  43. Box GEP, Hunter WG, Hunter JS (1978) Statistics for experimenters: an introduction to design, data analysis, and model building. Wiley, New York

    MATH  Google Scholar 

  44. Rosenfeld JP, Labkovsky E, Winograd M, Lui MA, Vandenboom C, Chedid E (2008) The complex trial protocol (CTP): a new, countermeasure-resistant, accurate, P300-based method for detection of concealed information. Psychophysiology 45:906–919

    Article  Google Scholar 

  45. Bajaj V, Pachori RB (2012) Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 16:1135–1142

    Article  Google Scholar 

  46. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. Accessed 7 Oct 2016

Download references

Author information

Authors and Affiliations

  1. Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India

    Navjot Saini, Saurabh Bhardwaj & Ravinder Agarwal

Authors
  1. Navjot Saini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saurabh Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ravinder Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Navjot Saini.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saini, N., Bhardwaj, S. & Agarwal, R. Classification of EEG signals using hybrid combination of features for lie detection. Neural Comput & Applic 32, 3777–3787 (2020). https://doi.org/10.1007/s00521-019-04078-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04078-z

Keywords

Search

Navigation