EEG-Based Subjects Identification Based on Biometrics of Imagined Speech Using EMD

  • Luis Alfredo MoctezumaEmail author
  • Marta Molinas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)


When brain activity ions, the potential for human capacities augmentation is promising. In this paper, EMD is used to decompose EEG signals during Imagined Speech in order to use it as a biometric marker for creating a Biometric Recognition System. For each EEG channel, the most relevant Intrinsic Mode Functions (IMFs) are decided based on the Minkowski distance, and for each IMF 4 features are computed: Instantaneous and Teager energy distribution and Higuchi and Petrosian Fractal Dimension. To test the proposed method, a dataset with 20 Subjects who imagined 30 repetitions of 5 words in Spanish, is used. Four classifiers are used for this task - random forest, SVM, naive Bayes, and k-NN - and their performances are compared. The accuracy obtained (up to 0.92 using Linear SVM) after 10-folds cross-validation suggest that the proposed method based on EMD can be valuable for creating EEG-based biometrics of imagined speech for Subject identification.


Biometric security Subject identification Imagined speech Electroencephalograms (EEG) Empirical Mode Decomposition (EMD) 



This work was supported by Enabling Technologies - NTNU, under the project “David versus Goliath: single-channel EEG unravels its power through adaptive signal analysis - FlexEEG”.


  1. 1.
    Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng. 4(2), R32 (2007)CrossRefGoogle Scholar
  2. 2.
    Desain, P., Farquhar, J., Haselager, P., Hesse, C., Schaefer, R.S.: What BCI research needs. In: Proceedings of the ACM CHI 2008 Conference on Human Factors in Computing Systems, Venice, Italy (2008)Google Scholar
  3. 3.
    Moctezuma, L.A., Carrillo, M., Villaseñor Pineda, L., Torres García, A.A.: Hacia la clasificación de actividad e inactividad lingüistica a partir de senales de electroencefalogramas (EEG). Res. Comput. Sci. 140, 135–149 (2017)Google Scholar
  4. 4.
    Torres-García, A.A., Reyes-García, C.A., Villaseñor-Pineda, L., Ramírez-Cortís, J.M.: Análisis de señales electroencefalográficas para la clasificación de habla imaginada. Revista mexicana de ingeniería biomédica 34(1), 23–39 (2013)Google Scholar
  5. 5.
    Nishimoto, T., Azuma, Y., Morioka, H., Ishii, S.: Individual identification by resting-state EEG using common dictionary learning. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10613, pp. 199–207. Springer, Cham (2017). Scholar
  6. 6.
    Brigham, K., Vijaya Kumar, B.V.K.: Subject identification from electroencephalogram (EEG) signals during imagined speech. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–8 (2010)Google Scholar
  7. 7.
    Jain, A.K., Ross, A., Uludag, U: Biometric template security: challenges and solutions. In: 2005 13th European Signal Processing Conference, pp. 1–4 (2005)Google Scholar
  8. 8.
    Ashby, C., Bhatia, A., Tenore, F., Vogelstein, J.: Low-cost electroencephalogram (EEG) based authentication. In: 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 442–445 (2011)Google Scholar
  9. 9.
    Palaniappan, R.: Electroencephalogram signals from imagined activities: a novel biometric identifier for a small population. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 604–611. Springer, Heidelberg (2006). Scholar
  10. 10.
    Del Pozo-Banos, M., Alonso, J.B., Ticay-Rivas, J.R., Travieso, C.M.: Electroencephalogram subject identification: a review. Expert. Syst. Appl. 41(15), 6537–6554 (2014)CrossRefGoogle Scholar
  11. 11.
    Steven, M.K.: Modern Spectral Estimation: Theory and Application. Signal Processing Series (1988)Google Scholar
  12. 12.
    Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995 (1998)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Rilling, G., Flandrin, P., Goncalves, P.: On empirical mode decomposition and its algorithms. In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, vol. 3 NSIP-03, Grado (I), pp. 8–11 (2003)Google Scholar
  14. 14.
    de Souza, D.B., Chanussot, J., Favre, A.-C.: On selecting relevant intrinsic mode functions in empirical mode decomposition: an energy-based approach. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 325–329 (2014)Google Scholar
  15. 15.
    Boutana, D., Benidir, M., Barkat, B.: On the selection of intrinsic mode function in EMD method: application on heart sound signal. In: 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), pp. 1–5 (2010)Google Scholar
  16. 16.
    Didiot, E., Illina, I., Fohr, D., Mella, O.: A wavelet-based parameterization for speechmusic discrimination. Comput. Speech Lang. 24(2), 341–357 (2010)CrossRefGoogle Scholar
  17. 17.
    Jabloun, F., Enis Cetin, A.: The Teager energy based feature parameters for robust speech recognition in car noise. In: 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 273–276 (1999)Google Scholar
  18. 18.
    Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Phys. D Nonlinear Phenom. 31, 277–283 (1988)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Petrosian, A.: Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns. In: Proceedings of the Eighth IEEE Symposium on Computer-Based Medical Systems, pp. 212–217 (1995)Google Scholar
  20. 20.
    Jasper, H.: Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 10, 370–375 (1958)CrossRefGoogle Scholar
  21. 21.
    Moctezuma, L.A., Molinas, M., Torres García, A.A., Villaseñor Pineda, L., Carrillo, M.: Towards an API for EEG-based imagined speech classification. In: International Conference on Time Series and Forecasting (2018)Google Scholar
  22. 22.
    Bertrand, O., Perrin, F., Pernier, J.: A theoretical justification of the average reference in topographic evoked potential studies. Electroencephalogr. Clin. Neurophysiol./Evoked Potentials Sect. 62(6), 462–464 (1985)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Engineering CyberneticsNorwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations