MLP for Electroencephalographic Signals Classification Using Different Adaptive Learning Algorithm

  • Roberto Sepúlveda
  • Oscar Montiel
  • Daniel Gutiérrez
  • Gerardo Díaz
  • Oscar Castillo
Part of the Studies in Computational Intelligence book series (SCI, volume 547)


For the identification of muscular pain caused by a puncture in the right arm and eye blink, electroencephalographic (EEG) signals are analyzed in the frequency and temporal domain. EEG activity was recorded from 15 subjects in range of 23–25 years of age, while pain is induced and during blinking. On the other hand, EEG was converted from time to frequency domain using the Fast Fourier Transform (FFT) for being classified by an Artificial Neural Network (ANN). Experimental results in the frequency and time domain using five adaptation algorithms show that both neural network architecture proposals for classification produce successful results.


  1. 1.
    Arias, G., Felipe, H.: Detección y clasificación de artefactos en señales EEG. In: Memorias de STSIVA’09. Universidad Tecnológica De Pereira (2009)Google Scholar
  2. 2.
    Chen, A., Rappelsberger, P.: Brain and human pain: topograph EEG amplitude and coherence mapping. Brain Topogr. 7(2), 129–140 (1994)CrossRefGoogle Scholar
  3. 3.
    Morchen, F.: Time Series Feature Extraction for Data Mining Using DWT and DFT. Philipps University Marburg, Marburg (2003)Google Scholar
  4. 4.
    Papadourakis, G., Vourkas, M., Micheloyannis, S., Jervis, B.: Use of artificial neural networks for clinical diagnosis. Math. Comput. Simul. 1996(40), 623–635 (1996)CrossRefGoogle Scholar
  5. 5.
    Pera, D.L., Svensson, P., Valeriani, M., Watanabe, I., Arendt-Nielsen, L., Chen, A.C.: Long-lasting effect evoked by tonic muscle pain on parietal EEG activity in humans. Clin. Neurophysiol. 111(12), 2130–2137 (2000)CrossRefGoogle Scholar
  6. 6.
    Sharbrough, F., Chatrian, G.-E., Lesser, R.P., Luders, H., Nuwer, M., Picton, T.W.: American electroencephalographic society guidelines for standard electrode position nomenclature. J. Clin. Neurophysiol 8(200), 2 (1991)Google Scholar
  7. 7.
    Sovierzoski, M., Argoud, F., De Azevedo, F.: Identifying eye blinks in EEG signal analysis. In: International Conference on Information Technology and Applications in Biomedicine (ITAB 2008), pp. 406–409 (2008)Google Scholar
  8. 8.
    Venkataramanan, S., Kalpakam, N.V.: Aiding the detection of Alzheimer’s disease in clinical electroencephalogram recording by selective denoising of ocular artifacts. In: International Conference on Communications, Circuits and Systems (ICCCAS2004), vol. 2, pp. 965–968 (2004)Google Scholar
  9. 9.
    Melin, P., Castillo, O.: An intelligent hybrid approach for industrial quality control combining neural networks, fuzzy logic and fractal theory. Inf. Sci. 177, 1543–1557 (2007)CrossRefGoogle Scholar
  10. 10.
    Mendoza, O., Melin, P., Castillo, O., Licea, G.: Type-2 fuzzy logic for improving training data and response integration in modular neural networks for image recognition. Lect. Notes Artif. Intell. 4529, 604–612 (2007)Google Scholar
  11. 11.
    Mendoza, O., Melin, P., Castillo, O.: Interval type-2 fuzzy logic and modular neural networks for face recognition applications. Appl. Soft Comput. J. 9, 1377–1387 (2009)CrossRefGoogle Scholar
  12. 12.
    Mendoza, O., Melin, P., Licea, G.: Interval type-2 fuzzy logic for edges detection in digital images. Int. J. Intell. Syst. 24, 1115–1133 (2009)CrossRefMATHGoogle Scholar
  13. 13.
    Pérez, M., Luis, J.: Comunicación con Computador mediante Señales Cerebrales. Aplicación a la Tecnología de la Rehabilitación. Ph.D. thesis, Universidad Politécnica de Madrid (2009)Google Scholar
  14. 14.
    Hirsch, L., Richard, B.: EEG basics. In: Atlas of EEG in Critical Care, pp. 1–7. Wiley (2010)Google Scholar
  15. 15.
    Chang, P.F., Arendt-Nielsen, L., Graven-Nielsen, T., Svensson, P., Chen, A.C.: Comparative EEG activation to skin pain and muscle pain induced by capsaicin injection. Int. J. Psychophysiol. 51(2), 117–126 (2004)CrossRefGoogle Scholar
  16. 16.
    De la O Chavez, J.R.: BCI para el control de un cursor basada en ondas cerebrales. Master’s thesis, Universidad Autónoma Metropolitana (2008)Google Scholar
  17. 17.
    Erfanian, A., Gerivany, M.: EEG signals can be used to detect the voluntary hand movements by using an enhanced resource-allocating neural network. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2001, vol. 1, pp. 721, 724 (2001)Google Scholar
  18. 18.
    Lin, J.-S., Chen, K.-C., Yang, W.-C.: EEG and eye-blinking signals through a brain-computer interface based control for electric wheelchairs with wireless scheme. In: 4th International Conference on New Trends in Information Science and Service Science (NISS) 2010, pp. 731–734Google Scholar
  19. 19.
    Haykin, S.: Neural networks a comprehensive foundation. Pearson Prentice Hall, Delhi (1999)MATHGoogle Scholar
  20. 20.
    Mitchell, T.M.: Machine Learning, Chap. 4. McGraw-Hill (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roberto Sepúlveda
    • 1
  • Oscar Montiel
    • 1
  • Daniel Gutiérrez
    • 1
  • Gerardo Díaz
    • 1
  • Oscar Castillo
    • 2
  1. 1.Instituto Politécnico Nacional, Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN)TijuanaMexico
  2. 2.Tijuana Institute of TechnologyTijuanaMexico

Personalised recommendations