Investigating the Impacts of Brain Conditions on EEG-Based Person Identification

  • Dinh Phung
  • Dat Tran
  • Wanli Ma
  • Tien Pham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 369)


Person identification using electroencephalogram (EEG) as biometric has been widely used since it is capable of achieving high identification rate. Brain conditions such as epilepsy and alcohol are some of problems that cause brain disorders in EEG signals, and hence they may have impacts on EEG-based person identification systems. However, this issue has not been investigated. In this paper, we perform person identification on two datasets, Australian and Alcoholism EEG, then compare the classification rates between epileptic and non-epileptic groups, and between alcoholic and non-alcoholic groups, to investigate the impacts of such brain conditions on the identification rates. Shannon (SEn), Spectral (SpEn), Approximate entropy (ApEn), Sample (SampEn) and Conditional (CEn) entropy are employed to extract features from these two datasets. Experimental results show that both epilepsy and alcohol actually have different impacts depending on feature extraction method used in the system.


Support Vector Machine Epileptic Seizure Person Identification Entropy Method Brain Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Marcel, S., Millán, Jl.R.: Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. Technical Report Idiap-RR-81-2005, IDIAP, 2005Google Scholar
  2. 2.
    Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley-Interscience (2007)Google Scholar
  3. 3.
    Mohammadi, G., Shoushtari, P., Ardekani, B.M., Shamsollahi, M.B.: Person identification by using ar model for EEG signals. World Acad. Sci. Eng. Technol. 1(11), 918–923 (2007)Google Scholar
  4. 4.
    Pham, T., Ma, W., Tran, D., Nguyen, P., Phung, D.: A study on the feasibility of using EEG signals for authentication purpose. In: ICONIP, pp. 562–569 (2013)Google Scholar
  5. 5.
    Shedeed, H.A.: A new method for person identification in a biometric security system based on brain EEG signal processing. WICT 1, 205–1210 (2011)Google Scholar
  6. 6.
    Abdullah, M.K., Subari, K.S., Loong, J.L.C., Ahmad, N.N.: Analysis of the EEG signal for a practical biometric system. Biomed. Eng. World Acad. Sci. Eng. Technol. 4(8), 944–949 (2010)Google Scholar
  7. 7.
    Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Prog. Biomed. 80(3), 187–194 (2005)CrossRefGoogle Scholar
  8. 8.
    Kumar, Y., Dewal, M.L.: Complexity measures for normal and epileptic EEG signals using ApEn, SampEn and SEN. IJCCT 2(7), 6–12 (2011)Google Scholar
  9. 9.
    Acharya, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.-H., Suri, J.S.: Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7, 401–408 (2012)Google Scholar
  10. 10.
    Srinivasan, V., Eswaran, C., Sriraam, N.: Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inform. Technol. Biomed. 11(3), 288–295 (2007)CrossRefGoogle Scholar
  11. 11.
    Vukkadala, S., Vijayalakshmi, S., Vijayapriya, S.: Automated detection of epileptic EEG using approximate entropy in elman networks. Int. J. Recent Trends Eng. 1(1), 307–312 (2009)Google Scholar
  12. 12.
    Zandi, A.S., Javidan, M., Dumont, G.A., Tafreshi, R.: Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans. Biomed. Eng. 57(7), 1639–1651 (2010)CrossRefGoogle Scholar
  13. 13.
    Porjesz, B., Begleiter, H.: Alcoholism and human electrophysiology. Alcohol Res. Health 27(2), 153–160 (2003)Google Scholar
  14. 14.
    Kendre, S., Janvale, G., Mehrotra, S.: Mental and behavioural disorders related to alcohol and their effects on EEG signals an overview. ProcediaSoc. Behav. Sci. 133(0), 116–121 (2014). International Conference on Trade, Markets and Sustainability (ICTMS-2013)Google Scholar
  15. 15.
    Kahkonen, S., Wilenius, J., Nikulin, V.V., Ollikainen, M., Ilmoniemi, R.J.: Alcohol reduces prefrontal cortical excitability in humans: a combined tms and eeg study. Neuropsychopharmacology 28(4), 747–754 (2003)CrossRefGoogle Scholar
  16. 16.
    Chaovalitwongse, W., Prokopyev, O., Pardalos, P.: Electroencephalogram (EEG) time series classification: applications in epilepsy. Ann. OR 148(1), 227–250 (2006)CrossRefzbMATHGoogle Scholar
  17. 17.
    Hope, A., Rosipal, R.: Measuring depth of anesthesia using electroencephalogram entropy rates. Technical report, Department of Theoretical Methods, Slovak Academy of Sciences, Slovak Republic, 2001Google Scholar
  18. 18.
    Litscher, G.: Electroencephalogram-entropy and acupuncture. Anesth. Analg. 102(6), 1745–1751 (2006)CrossRefGoogle Scholar
  19. 19.
    Song, Y., Liò, P.: A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. Biomed. Sci. Eng. 3, 556–567 (2010)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Vollala, S., Gulla, K.: Automatic detection of epilepsy EEG using neural networks. Int. J. Internet Comput. (IJIC) 1(3), 68–72 (2012)Google Scholar
  21. 21.
    Phung, D., Tran, D., Ma, W., Nguyen, P., Pham, T.: Using shannon entropy as EEG signal feature for fast person identification. In: Proceedings of 22nd European Symposim on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), vol. 4, pp. 413–418 (2014)Google Scholar
  22. 22.
    Phung, D., Tran, D., Ma, W., Nguyen, P., Pham, T.: Investigating the impacts of epilepsy on EEG-based person identification systems. In 2014 International Joint Conference on Neural Networks (IJCNN), pp. 3644–3648. IEEE (2014)Google Scholar
  23. 23.
    Hunter, M., Smith, R.L.L., Hyslop, W., Rosso, O.A., Gerlach, R., Rostas, J.A.P., Williams, D.B., Henskens, F.: The Australian EEG database. Clin. EEG Neurosci. 36(2), 76–81 (2005)CrossRefGoogle Scholar
  24. 24.
    Begleiter, H.: EEG databaseGoogle Scholar
  25. 25.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based BCIs. J. Neural Eng. 4(2) (2007)Google Scholar
  26. 26.
    Nguyen, P., Tran, D., Huang, X., Sharma, D.: A proposed feature extraction method for EEG-based person identification. In: International Conference on Artificial Intelligence (2012)Google Scholar
  27. 27.
    He, Z.Y., Jin, L.W.: Activity recognition from acceleration data using AR model representation and SVM. In: Machine Learning and Cybernetics, January 2008Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Education, Science, Technology and MathematicsUniversity of CanberraCanberraAustralia

Personalised recommendations