A Recurrence-Based Approach for Feature Extraction in Brain-Computer Interface Systems

  • Luisa F. S. Uribe
  • Filipe I. Fazanaro
  • Gabriela Castellano
  • Ricardo Suyama
  • Romis Attux
  • Eleri Cardozo
  • Diogo C. Soriano
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 103)


The feature extraction stage is one of the main tasks underlying pattern recognition, and, is particularly important for designing Brain-Computer Interfaces (BCIs), i.e. structures capable of mapping brain signals in commands for external devices. Within one of the most used BCIs paradigms, that based on Steady State Visual Evoked Potentials (SSVEP), such task is classically performed in the spectral domain, albeit it does not necessarily provide the best achievable performance. The aim of this work is to use recurrence-based measures in an attempt to improve the classification performance obtained with a classical spectral approaches for a five-command SSVEP-BCI system. For both recurrence and spectral spaces, features were selected using a cluster measure defined by the Davies-Bouldin index and the classification stage was based on linear discriminant analysis. As the main result, it was found that the threshold \(\varepsilon \) of the recurrence plot, chosen so as to yield a recurrence rate of 2.5 %, defined the key discriminant feature, typically providing a mean classification error of less than 2 % when information from 4 electrodes was used. Such classification performance was significantly better than that attained using spectral features, which strongly indicates that RQA is an efficient feature extraction technique for BCI.


Linear Discriminant Analysis Classification Error Spectral Domain Recurrence Plot Recurrence Quantification Analysis 
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.



L.F.S. Uribe thanks CAPES for financial support. D.C. Soriano and R. Suyama thank UFABC and FAPESP (Grant number 2012/50799-2) for the financial support. F.I. Fazanaro thanks FAPESP (Grant number 2012/09624-4) for the financial support. R. Attux thanks CNPq for financial support. All authors are grateful to FINEP for funding the DesTine project (process number 01.10.0449.00).


  1. 1.
    Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfacing: Principles and Practice. Oxford University Press, New York (2012)Google Scholar
  2. 2.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002). doi: 10.1016/S1388-2457(02)00057-3 CrossRefGoogle Scholar
  3. 3.
    Millán, J.R., Rupp, R., Mueller-Putz, G., Murray-Smith, R., Giugliemma, C., Tangermann, M., Vidaurre, C., Cincotti, F., Kubler, A., Leeb, R., Neuper, C., Mueller, K.R., Mattia, D.: Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Front. Neurosci. 4(161), 1–15 (2010). doi: 10.3389/fnins.2010.00161 Google Scholar
  4. 4.
    Sobani, Z.A., Quadri, S.A., Enam, S.A.: Stem cells for spinal cord regeneration: current status. Surg. Neurol. Int. 1(1), 93 (2010). doi: 10.4103/2152-7806.74240 CrossRefGoogle Scholar
  5. 5.
    Zhu, D., Bieger, J., Garcia Molina, G., Aarts, R.M.: A survey of stimulation methods used in SSVEP-based BCIs. In: Computational Intelligence and Neuroscience 2010, 702,357 (2010). doi: 10.1155/2010/702357
  6. 6.
    Marwan, N., Romano, M.C., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems. Phys. Rep. 438(5–6), 237–329 (2007). doi: 10.1016/j.physrep.2006.11.001 CrossRefMathSciNetGoogle Scholar
  7. 7.
    Marwan, N.: How to avoid potential pitfalls in recurrence plot based data analysis. Int. J. Bifurcat. Chaos 21(4), 1003–1017 (2011). doi: 10.1142/S0218127411029008 CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Zbilut, J.P., Zaldívar-Comenges, J.M., Strozzi, F.: Recurrence quantification based Liapunov exponents for monitoring divergence in experimental data. Phys. Lett. A 297(3–4), 173–181 (2002). doi: 10.1016/S0375-9601(02)00436-X CrossRefzbMATHGoogle Scholar
  9. 9.
    Soriano, D.C., Suyama, R., Ando, R.A., Attux, R., Duarte, L.T.: Blind source separation in the context of deterministic signals. In: Eisencraft, M., Suyama, R., Attux, R. (eds.) Chaotic Signals in Digital Communications, pp. 325–348. CRC Press, Boca Raton (2013). doi: 10.1201/b15473-13 Google Scholar
  10. 10.
    Semmlow, J.: Signals and Systems for Bioengineers, 2nd edn. Academic Press, Amsterdam (2011)Google Scholar
  11. 11.
    Stoica, P., Moses, R.L.: Introduction to Spectral Analysis, Prentice-Hall, Upper Saddle River (1997)Google Scholar
  12. 12.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–1(2), 224–227 (1979). doi: 10.1109/TPAMI.1979.4766909. CrossRefGoogle Scholar
  13. 13.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Academic Press, New York (1999)Google Scholar
  14. 14.
    Perez, J.L.M., Cruz, A.B.: Linear discriminant analysis on brain computer interface. In: IEEE International Symposium on Intelligent Signal Processing, 2007. WISP 2007, pp. 1–6 (2007). doi: 10.1109/WISP.2007.4447590
  15. 15.
    Prichard, D., Theiler, J.: Generalized redundancies for time series analysis. Phys. D 84(3–4), 476–493 (1995). doi: 10.1016/0167-2789(95)00041-2 CrossRefGoogle Scholar
  16. 16.
    Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Phys. D 9(1–2), 189–208 (1983). doi: 10.1016/0167-2789(83)90298-1 CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Acharya, R., Faust, O., Kannathal, N., Chua, T., Laxminarayan, S.: Non-linear analysis of EEG signals at various sleep stages. Comput. Methods Programs Biomed. 80(1), 37–45 (2005). doi: 10.1016/j.cmpb.2005.06.011 CrossRefGoogle Scholar
  18. 18.
    Stam, C.J.: Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin. Neurophysiol. 116(10), 2266–2301 (2005). doi: 10.1016/j.clinph.2005.06.011 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luisa F. S. Uribe
    • 1
  • Filipe I. Fazanaro
    • 3
  • Gabriela Castellano
    • 2
  • Ricardo Suyama
    • 3
  • Romis Attux
    • 1
  • Eleri Cardozo
    • 1
  • Diogo C. Soriano
    • 3
  1. 1.Department of Computer Engineering and Industrial Automation (DCA/FEEC)University of Campinas (UNICAMP)CampinasBrazil
  2. 2.Neurophysics Group, Deptartment of Cosmic Rays and ChronologyInstitute of Physics Gleb Wataghin (IFGW)CampinasBrazil
  3. 3.Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS)Universidade Federal do ABCSanto AndréBrazil

Personalised recommendations