A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer’s Disease EEG Data

  • Elisa Capecci
  • Francesco Carlo Morabito
  • Maurizio Campolo
  • Nadia Mammone
  • Domenico Labate
  • Nikola Kasabov
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 37)


The paper presents a feasibility analysis of a novel Spiking Neural Network (SNN) architecture called NeuCube [10] for classification and analysis of functional changes in brain activity of Electroencephalography (EEG) data collected amongst two groups: control and Alzheimer’s Disease (AD). Excellent classification results of 100% test accuracy have been achieved and these have also been compared with traditional machine learning techniques. Outputs confirmed that the NeuCube is better suited to model, classify, interpret and understand EEG data and the brain processes involved. Future applications of a NeuCube model are discussed including its use as an indicator of the early onset of Mild Cognitive Impairment(MCI) to study degeneration of the pathology toward AD.


Spiking Neural Networks NeuCube EEG data classification Alzheimer’s Disease 


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  1. 1.
    Benuskova, L., Kasabov, N.: Computational Neurogenetic Modelling. Springer, NY (2007)CrossRefGoogle Scholar
  2. 2.
    Delbruck, T.: jaer open source project (2007), (April 14, 2014)
  3. 3.
    Fawcett, T.: An introduction to roc analysis. Pattern Recognition Letters 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D.: Random forest-based similarity measures for multi-modal classification of alzheimer’s disease. NeuroImage 65, 167–175 (2013)CrossRefGoogle Scholar
  5. 5.
    Indiveri, G., Linares-Barranco, B., Hamilton, T.J., Van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S.C., Dudek, P., Häfliger, P., Renaud, S., et al.: Neuromorphic silicon neuron circuits. Frontiers in Neuroscience 5 (2011)Google Scholar
  6. 6.
    Izhikevich, E.M.: Polychronization: Computation with spikes. Neural Computation 18(2), 245–282 (2006)zbMATHMathSciNetCrossRefGoogle Scholar
  7. 7.
    Kasabov, N.: Evolving connectionist systems: The knowledge engineering approach. Springer (2007)Google Scholar
  8. 8.
    Kasabov, N., Capecci, E.: Spiking neural network methodology for modelling, recognition and understanding of eeg spatio-temporal data measuring cognitive processes during mental tasks. Information Sciences (2014)Google Scholar
  9. 9.
    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
  10. 10.
    Kasabov, N.K.: Neucube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks 52, 62–76 (2014)CrossRefGoogle Scholar
  11. 11.
    Labate, D., Foresta, F., Morabito, G., Palamara, I., Morabito, F.C.: Entropic measures of eeg complexity in alzheimer’s disease through a multivariate multiscale approach. IEEE Sensors Journal 13(9), 3284–3292 (2013)CrossRefGoogle Scholar
  12. 12.
    Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., Fox, P.T.: Automated talairach atlas labels for functional brain mapping. Human Brain Mapping 10(3), 120–131 (2000)CrossRefGoogle Scholar
  13. 13.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: Span: Spike pattern association neuron for learning spatio-temporal sequences. International Journal of Neural Systems (2012)Google Scholar
  14. 14.
    Morabito, F.C., Labate, D., Bramanti, A., La Foresta, F., Morabito, G., Palamara, I., Szu, H.H.: Enhanced compressibility of eeg signal in alzheimer’s disease patients. IEEE Sensors Journal 13(9), 3255–3262 (2013)CrossRefGoogle Scholar
  15. 15.
    Morabito, F.C., Labate, D., La Foresta, F., Bramanti, A., Morabito, G., Palamara, I.: Multivariate multi-scale permutation entropy for complexity analysis of alzheimer’s disease eeg. Entropy 14(7), 1186–1202 (2012)zbMATHCrossRefGoogle Scholar
  16. 16.
    Ortiz, A., Górriz, J.M., Ramírez, J., Martínez-Murcia, F.J.: Lvq-svm based cad tool applied to structural mri for the diagnosis of the alzheimer’s disease. Pattern Recognition Letters 34(14), 1725–1733 (2013)CrossRefGoogle Scholar
  17. 17.
    Platel, M.D., Schliebs, S., Kasabov, N.: Quantum-inspired evolutionary algorithm: a multimodel eda. IEEE Transactions on Evolutionary Computation 13(6), 1218–1232 (2009)CrossRefGoogle Scholar
  18. 18.
    Pritchard, C., Mayers, A., Baldwin, D.: Changing patterns of neurological mortality in the 10 major developed countries - 1979 - 2010. Public Health 127(4), 357–368 (2013)CrossRefGoogle Scholar
  19. 19.
    Rodriguez, G., Copello, F., Vitali, P., Perego, G., Nobili, F.: Eeg spectral profile to stage alzheimer’s disease. Clinical Neurophysiology 110, 1831–1837 (1999)CrossRefGoogle Scholar
  20. 20.
    Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models. Neural Networks 22(5), 623–632 (2009)CrossRefGoogle Scholar
  21. 21.
    Shu, H., Nan, B., Koeppe, R., et al.: Multiple testing for neuroimaging via hidden markov random field. arXiv preprint arXiv:1404.1371 (2014)Google Scholar
  22. 22.
    Song, Q., Kasabov, N.: Ecm - a novel on-line, evolving clustering method and its applications. In: Posner, M.I. (ed.) Foundations of Cognitive Science, pp. 631–682. The MIT Press (2001)Google Scholar
  23. 23.
    Song, S., Miller, K., Abbott, L.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3, 919–926 (2000)CrossRefGoogle Scholar
  24. 24.
    Talairach, J., Tournoux, P.: Co-planar stereotaxic atlas of the human brain. 3-dimensional proportional system: an approach to cerebral imaging. Thieme (1988)Google Scholar
  25. 25.
    Taylor, D., Scott, N., Kasabov, N., Capecci, E., Tu, E., Saywell, N., Chen, Y., Hu, J., Hou, Z.G.: Feasibility of neucube snn architecture for detecting motor execution and motor intention for use in bciapplications. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 3221–3225 (July 2014)Google Scholar
  26. 26.
    Thorpe, S., Gautrais, J.: Rank order coding. In: Computational Neuroscience, pp. 113–118. Springer (1998)Google Scholar
  27. 27.
    Tu, E., Kasabov, N., Othman, M., Li, Y., Worner, S., Yang, J., Jia, Z.: Neucube(st) for spatio-temporal data predictive modelling with a case study on ecological data. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 638–645 (July 2014)Google Scholar
  28. 28.
    Zhang, Y., Wang, S., Dong, Z.: Classification of alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Progress in Electromagnetics Research 144, 171–184 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Elisa Capecci
    • 1
  • Francesco Carlo Morabito
    • 2
  • Maurizio Campolo
    • 2
  • Nadia Mammone
    • 2
  • Domenico Labate
    • 2
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  2. 2.DICEAM - Mediterranea University of Reggio CalabriaReggio CalabriaItaly

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