References

• 1. 1)
     • 27. Kumari, P., Vaish, A.: ‘Information-theoretic measures on intrinsic mode function for the individual identification using EEG sensors’, IEEE Sens. J., 2015, 15, (9), pp. 4950–4960.
  2. 2)
     • 16. Del Pozo-Banos, M., Alonso, J.B., Ticay-Rivas, J.R., et al: ‘Electroencephalogram subject identification: a review’, Expert Syst. Appl., 2014, 41, pp. 6537–6554.
  3. 3)
     • 24. La Rocca, D., Campisi, P., Vegso, B., et al: ‘Human brain distinctiveness based on EEG spectral coherence connectivity’, IEEE Trans. Biomed. Eng., 2014, 61, (9), pp. 2406–2412.
  4. 4)
     • 11. Del Pozo-Banos, M., Travieso, C.M., Weidemann, C.T., et al: ‘EEG biometric identification: a thorough exploration of the time–frequency domain’, J. Neural Eng., 2015, 12, (5), pp. 1–23.
  5. 5)
     • 40. Richman, J.S., Moorman, J.R.: ‘Physiological time-series analysis using approximate entropy and sample entropy’, Am. J. Physiol. Heart Circ. Physiol., 2000, 278, pp. 2039–2049.
  6. 6)
     • 41. Chen, W., Wang, Z., Xie, H., et al: ‘Characterization of surface EMG signal based on fuzzy entropy’, IEEE Trans. Neural Syst. Rehabil. Eng., 2007, 15, pp. 266–272.
  7. 7)
     • 23. Poulos, M., Rangoussi, M., Alexandris, N.: ‘Neural network based person identification using EEG features’. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 1999.
  8. 8)
     • 13. Sanei, S., Chambers, J.A.: ‘EEG signal processing’ (John Wiley & Sons, 2013).
  9. 9)
     • 26. Rodrigues, D., Silva, G.F.A., Papa, J.P., et al: ‘EEG-based person identification through binary flower pollination algorithm’, Expert Syst. Appl., 2016, 62, pp. 81–90.
  10. 10)
      • 8. Boulgouris, N.V., Konstantinos, N.P., Evangelia, M.-T.: ‘Biometrics: theory, methods, and applications’ (John Wiley & Sons, 2009).
  11. 11)
      • 22. Paranjape, R.B., Mahovsky, J., Benedicenti, L., et al: ‘The electroencephalogram as a biometric’. Canadian Conf. on Electrical & Computer Engineering, 2001.
  12. 12)
      • 19. Hu, J.F.: ‘New biometric approach based on motor imagery EEG signals’. Proc. of the Int. Conf. on Future BioMedical Information Engineering, 2009.
  13. 13)
      • 3. Kumar, S.P., Sriraam, N., Benakop, P.G., et al: ‘Entropies based detection of epileptic seizures with artificial neural network classifiers’, Expert Syst. Appl., 2010, 37, pp. 3284–3291.
  14. 14)
      • 30. Dauwels, J., Vialatte, F., Cichocki, A.: ‘Diagnosis of alzheimer's disease from EEG signals: where are we standing?’, Curr. Alzheimer Res., 2010, 7, pp. 487–505.
  15. 15)
      • 5. Song, Y., Crowcroft, J., Zhang, J.: ‘Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine’, J. Neurosci. Methods, 2012, 210, pp. 132–146.
  16. 16)
      • 2. Yun, K., Park, H.K., Kwon, D.H., et al: ‘Decreased cortical complexity in methamphetamine abusers’, Psychiatry Res., 2012, 201, pp. 226–232.
  17. 17)
      • 9. Pham, T., Ma, W., Tran, D., et al: ‘A study on the feasibility of using EEG signals for authentication purpose’, Neural Inf. Process., 2013, 8227, pp. 562–569.
  18. 18)
      • 36. Phung, D., Tran, D., Ma, W., et al: ‘Using Shannon entropy as EEG signal feature for fast person identification’. European Symp. on Artificial Neural Networks, 2014.
  19. 19)
      • 10. Yang, S., Deravi, F.: ‘Novel HHT-based features for biometric identification using EEG signals’. Int. Conf. on Pattern Recognition, 2014, pp. 1922–1927.
  20. 20)
      • 21. Yeom, S.K., Suk, H.I., Lee, S.W.: ‘Person authentication from neural activity of face-specific visual self-representation’, Pattern Recognit., 2013, 46, (4), pp. 1159–1169.
  21. 21)
      • 29. Blasco, J.S., Iáñez, E., Ubeda, A., et al: ‘Visual evoked potential-based brain–machine interface applications to assist disabled people’, Expert Syst. Appl., 2012, 39, pp. 7908–7918.
  22. 22)
      • 31. Wang, S., Wu, G., Zhu, Y.: ‘Analysis of affective effects on steady-state visual evoked potential responses’, Intell. Auton. Syst., 2013, 12, pp. 757–766.
  23. 23)
      • 32. Mousa, M.F., Cubbidge, R.P., Al-Mansouri, F., et al: ‘Evaluation of hemifield sector analysis protocol in multifocal visual evoked potential objective perimetry for the diagnosis and early detection of glaucomatous field defects’, Korean J. Ophthalmol., 2014, 28, (1), pp. 49–65.
  24. 24)
      • 33. Abo-Zahhad, M., Ahmed, S.M., Abbas, S.N.: ‘A new multi-level approach to EEG based human authentication using eye blinking’, Pattern Recognit. Lett., 2015, 82, pp. 1–10.
  25. 25)
      • 38. Shannon, C.E.: ‘A mathematical theory of communication’, ACM SIGMOBILE Mob. Comput. Commun. Rev., 2001, 5, pp. 3–55.
  26. 26)
      • 6. Zaylaa, A.J., Harb, A., Khatib, F.I., et al: ‘Entropy complexity analysis of electroencephalographic signals during pre-ictal, seizure and post-ictal brain events’. Int. Conf. on Advances in Biomedical Engineering, 2015, pp. 134–137.
  27. 27)
      • 42. Chen, W., Zhuang, J., Yu, W., et al: ‘Measuring complexity using FuzzyEn, ApEn, and SampEn’, Med. Eng. Phys., 2009, 31, pp. 61–68.
  28. 28)
      • 17. Palaniappan, R., Raveendran, P.: ‘Individual identification technique using visual evoked potential signals’, Electron. Lett., 2002, 38, pp. 1634–1635.
  29. 29)
      • 25. Xiao, D., Hu, J.F.: ‘Identification of motor imagery EEG signal’. Proc. of the Int. Conf. on Biomedical Engineering and Computer Science, 2010.
  30. 30)
      • 34. Hu, J., Xiao, D., Mu, Z.: ‘Application of energy entropy in motor imagery EEG classification’, Int. J. Digit. Content Technol. Appl., 2009, 3, (2), pp. 83–90.
  31. 31)
      • 18. Hu, J.F., Mu, Z.D., Wang, P.: ‘Multi-feature authentication system based on event evoked electroencephalogram’, J. Med. Imaging Health Inf., 2015, 5, (4), pp. 862–870.
  32. 32)
      • 15. Phung, D., Tran, D., Ma, W., et al: ‘Conditional entropy approach to multichannel EEG-based person identification’. Int. Joint Conf., Advances in Intelligent Systems and Computing, 2015.
  33. 33)
      • 43. Hua, S., Sun, Z.: ‘Support vector machine approach for protein subcellular localization prediction’, Bioinformatics, 2001, 17, (8), pp. 721–728.
  34. 34)
      • 20. Liew, S.H., Choo, Y.H., Low, Y.F., et al: ‘Identifying visual evoked potential (VEP) electrodes setting for person authentication’, Int. J. Adv. Soft Comput. Appl., 2015, 7, (3), pp. 85–99.
  35. 35)
      • 4. Sharma, R., Pachori, R.B., Acharya, U.R.: ‘Application of entropy measures on intrinsic mode functions for the automated identification of self-photo electroencephalogram signals’, Entropy, 2015, 17, pp. 669–691.
  36. 36)
      • 7. Bhattacharyya, D., Ranjan, R., Farkhod Alisherov, A., et al: ‘Biometric authentication: a review’, Int. J. u-and e-Serv. Sci. Technol., 2009, 2, pp. 13–28.
  37. 37)
      • 37. Kannathal, N., Choo, M.L., Acharya, U.R., et al: ‘Entropies for detection of epilepsy in EEG’, Comput. Methods Programs Biomed., 2005, 80, pp. 187–194.
  38. 38)
      • 12. Mao, C., Hu, B., Wang, M., et al: ‘EEG-based biometric identification using local probability centers’. Int. Joint Conf. on Neural Networks, 2015.
  39. 39)
      • 14. Marcel, S., Del Millan, J.R.: ‘Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, pp. 743–752.
  40. 40)
      • 35. Hu, J.F.: ‘Biometric system based on EEG signals by feature combination’. Int. Conf. on Measuring Technology and Mechatronics Automation, 2010.
  41. 41)
      • 1. Azarnoosh, M., Nasrabadi, A.M., Mohammadi, M.R., et al: ‘Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: symbolic dynamics’, Chaos Solitons Fractals, 2011, 44, pp. 1054–1062.
  42. 42)
      • 28. Das, R., Maiorana, E., La Rocca, D., et al: ‘EEG biometrics for user recognition using visually evoked potentials’. Int. Conf. of the IEEE, Biometrics Special Interest Group, 2015.
  43. 43)
      • 39. Pincus, S.M.: ‘Approximate entropy as a measure of system complexity’, Proc. Natl. Acad. Sci., 1991, 88, pp. 2297–2301.