Recognition and Real-Time Detection of Blinking Eyes on Electroencephalographic Signals Using Wavelet Transform

  • Renato Salinas
  • Enzo Schachter
  • Michael Miranda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


In this paper we study the detection of a specific pattern associated with the blinking of an eye in real time using electroencephalogram (EEG) signals of a single channel. This paper takes into account the theoretical and practical principles enabling the design and implementation of a system for real-time detection of time location, regardless of scale and multiple incidences. By using wavelet transform it permits us the fulfillment of our objective. The multiple detection and real-time operation is achieved by working with a pop-up window giving the projection of an ongoing analysis of the signal sampled by the EEG.


biological signals electroencephalogram EEG brain computer interface BCI eye blink detection pattern recognition wavelet transform 


  1. 1.
    Gloor, P.: Hans Berger on the Electroencephalogram of Man. Elsevier Publishing Company, Amsterdam (1969)Google Scholar
  2. 2.
    Fisch, B.: EEG PRIMER Basic principles of digital and analog EEG, 3rd edn. Elsevier Academic Press (1999) ISBN: 0-444-82147-3Google Scholar
  3. 3.
    Binnie, C., Cooper, R., Mauguire, F., Osselton, J., Prior, P., Tedman, B.: Clinical Neurophysiology. Elsevier Academic Press (2003) ISBN: 0-444-51257-8Google Scholar
  4. 4.
    Polkko, J.: A Method for Detecting Eye Blinks from Single-Channel Biopotential Signal in the Intensive Care Unit. Master’s Thesis (2007)Google Scholar
  5. 5.
    Slep, M.: Single Trial Analysis of EEG Signals, COS 497/498 (2003)Google Scholar
  6. 6.
    Bayliss, J.: A Flexible Brain-Computer Interface. Ph.D. Thesis, Computer Science Dept., U. Rochester (August 2001)Google Scholar
  7. 7.
    Chambayil, B., Singla, R., Jha, R.: EEG Eye Blink Classification Using Neural Network. In: Proceedings of the World Congress on Engineering, WCE 2010, London, U.K., vol. I (June 2010)Google Scholar
  8. 8.
    Walker, J.S.: Fourier Analysis and Wavelet Analysis. University of Wiconsin-Eau Claire. Notices of the Ams. 44(6) (1997) Google Scholar
  9. 9.
    Aeschbach, D., Borb’ely, A.A.: All-night dynamics of the human sleep EEG. J. Sleep Res. 2(2), 70–81 (1993)CrossRefGoogle Scholar
  10. 10.
    Gilmore, R.L.: American Electroencephalographic Society guidelines in electroencephalography, evoked potentials, and polysomnography. J. Clin. Neurophysiol. 11, 147 (1994)Google Scholar
  11. 11.
    Sharbrough, F., Chatrian, G.-E., Lesser, R.P., Lüders, H., Nuwer, M., Picton, T.W.: American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature. J. Clin. Neurophysiol. 8, 200–202 (1991)CrossRefGoogle Scholar
  12. 12.
    Divjak, M., Bischof, H.: Eye blink based fatigue detection for prevention of Computer Vision Syndrome. In: IAPR Conference on Machine Vision Applications (MVA 2009), Tokyo, Japan, May 20-22, pp. 350–353 (2009)Google Scholar
  13. 13.
    Mallat, S.: A theory for multiresolution signal decomposition: the wavelet repre-sentation. IEEE Pattern Anal. and Machine Intell. 11(7), 674–693 (1989)zbMATHCrossRefGoogle Scholar
  14. 14.
    Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.M.: Wavelet Toolbox for use with Matlab. The MathWorks Inc. EEUU (2004)Google Scholar
  15. 15.
    González, R., Woods, R.: Digital Image Processing, 3rd edn. Prentice-Hall (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Renato Salinas
    • 1
  • Enzo Schachter
    • 1
  • Michael Miranda
    • 2
  1. 1.Departamento de Ingeniería MecánicaUniversidad de Santiago de ChileSantiagoChile
  2. 2.Programa de Doctorado en Automatización, Facultad de IngenieríaUniversidad de Santiago de ChileSantiagoChile

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