Spatio-temporal EEG Data Classification in the NeuCube 3D SNN Environment: Methodology and Examples

  • Nikola Kasabov
  • Jin Hu
  • Yixiong Chen
  • Nathan Scott
  • Yulia Turkova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)


A vast amount of complex spatio-temporal brain data, such as EEG-, have been accumulated. Technological advances in many disciplines rely on the proper analysis, understanding and utilisation of these data. In order to address this great challenge, the paper utilizes the recently introduced by one of the authors 3D spiking neural network environment called NeuCube for spatio-temporal EEG data classification. A methodology is proposed and illustrated on two small-scale examples: classifying EEG data for music- versus noise perception, and person identification based on music perception. Future development and usage of the NeuCube environment can be expected to significantly further the creation of novel brain-computer interfaces, cognitive robotics and medical engineering devices.


EEG spatio-temporal data spiking neural networks music perception NeuCube 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zillies, K., Amunts, K.: Centenary of Brodmann’s map – conception and fate. Nature Reviews Neuroscience 11, 139–145 (2010)CrossRefGoogle Scholar
  2. 2.
    Talairach, J., Tournoux, P.: Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System - an Approach to Cerebral Imaging. Thieme Medical Publishers, NY (1988)Google Scholar
  3. 3.
    Evans, A.C., Collins, D.L., Mills, S.R., et al.: 3D statistical neuroanatomical models from 305 MRI volumes. In: Proc. IEEE-Nuclear Science Symp. Medical Imaging Conference, pp. 1813–1817 (1993)Google Scholar
  4. 4.
    Toga, A., Thompson, P., Mori, S., et al.: Towards multimodal atlases of the human brain. Nature Reviews Neuroscience 7, 952–966 (2006)CrossRefGoogle Scholar
  5. 5.
    Abeles, M.: Corticonics. Cambridge University Press, NY (1991)CrossRefGoogle Scholar
  6. 6.
    Fiasché, M., Schliebs, S., Nobili, L.: Integrating Neural Networks and Chaotic Measurements for Modelling Epileptic Brain. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part I. LNCS, vol. 7552, pp. 653–660. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), 1–15 (2007)CrossRefGoogle Scholar
  8. 8.
    Stam, C.J.: Functional connectivity patterns of human magnetoencephalographic recordings: A small-world network? Neurosci. Lett. 355, 25–28 (2004)CrossRefGoogle Scholar
  9. 9.
    De Charms, R.C.: Applications of real-time fMRI. Nature Reviews Neuroscience 9, 720–729 (2008)CrossRefGoogle Scholar
  10. 10.
    Mitchel, T., Hutchinson, R., et al.: Learning to Decode Cognitive States from Brain Images. Machine Learning 57, 145–175 (2004)CrossRefGoogle Scholar
  11. 11.
    Broderson, K., Wiech, K., Lomakina, E., et al.: Decoding the perception of pain from fMRI using multivariate pattern analysis. NeuroImage 63, 1162–1170 (2012)CrossRefGoogle Scholar
  12. 12.
    Hawrylycz, M., et al.: An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012)CrossRefGoogle Scholar
  13. 13.
    Gerstner, W., Sprekeler, H., Deco, G.: Theory and simulation in neuroscience. Science 338, 60–65 (2012)CrossRefGoogle Scholar
  14. 14.
    Song, S., Miller, K., Abbott, L., et al.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3, 919–926 (2000)CrossRefGoogle Scholar
  15. 15.
    Thorpe, S., Gautrais, J.: Rank order coding. Comput. Neuroscience: Trends in Research 13, 113–119 (1998)CrossRefGoogle Scholar
  16. 16.
    Maass, W., Natschlaeger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Izhikevich, E.: Polychronization: Computation with Spikes. Neural Computation 18, 245–282 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Belatreche, A., Maguire, L.P., McGinnity, M.: Advances in Design and Application of Spiking Neural Networks. Soft Comput. 11(3), 239–248 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Gerstner, W.: What’s different with spiking neurons? In: Mastebroek, H., Vos, H. (eds.) Plausible Neural Networks for Biological Modelling, pp. 23–48. Kluwer Academic Publishers (2001)Google Scholar
  20. 20.
    Lichtsteiner, P., Posch, C., Delbruck, T.: A 128x128 120dB 30mW Asynchronous Vision Sensor that Responds to Relative Intensity Changes. ISSCC Digest of Technical Papers, pp. 508–509 (2006)Google Scholar
  21. 21.
    Liu, S.C., Delbruck, T.: Neuromorphic sensory systems. Curr. Opinion in Neurobiology 20(3), 288–295 (2010)CrossRefGoogle Scholar
  22. 22.
    Benuskova, L., Kasabov, N.: Computational neuro-genetic modelling. Springer, New York (2007)CrossRefGoogle Scholar
  23. 23.
    Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neur. Netw. 23(1), 16–19 (2010)CrossRefGoogle Scholar
  24. 24.
    Furber, S.: To Build a Brain. IEEE Spectrum 49(8), 39–41 (2012)CrossRefGoogle Scholar
  25. 25.
    Indiveri, G., Horiuchi, T.K.: Frontiers in neuromorphic engineering. Frontiers in Neuroscience 5, 1–2 (2011)Google Scholar
  26. 26.
    Kasabov, N., Dhoble, K., Nuntalid, N., Indiveri, G.: Dynamic Evolving Spiking Neural Networks for On-line Spatio- and Spectro-Temporal Pattern Recognition. Neural Networks 41, 188–201 (2013)CrossRefGoogle Scholar
  27. 27.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences. Int. J. of Neural Systems 22(4), 1–16 (2012)Google Scholar
  28. 28.
    Kasabov, N.: NeuCube EvoSpike Architecture for Spatio-Temporal Modelling and Pattern Recognition of Brain Signals. In: Mana, N., Schwenker, F., Trentin, E. (eds.) ANNPR 2012. LNCS (LNAI), vol. 7477, pp. 225–243. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  29. 29.
    Kasabov, N.: Evolving connectionist systems: The knowledge engineering approach. Springer (2007)Google Scholar
  30. 30.
    Koessler, L., Maillard, L., Benhadid, A., et al.: Automated cortical projection of EEG sensors: Anatomical correlation via the international 10–10 system. NeuroImage 46, 64–72 (2006)CrossRefGoogle Scholar
  31. 31.
    Kasabov, N.: Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition. In: Liu, J., Alippi, C., Bouchon-Meunier, B., Greenwood, G.W., Abbass, H.A. (eds.) WCCI 2012. LNCS, vol. 7311, pp. 234–260. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nikola Kasabov
    • 1
  • Jin Hu
    • 2
  • Yixiong Chen
    • 2
  • Nathan Scott
    • 1
  • Yulia Turkova
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyNew Zealand
  2. 2.State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations