Intelligent Control Strategies for Neurostimulation

  • Javier Echauz
  • Hiram Firpi
  • George Georgoulas
Part of the Intelligent Systems, Control, and Automation: Science and Engineering book series (ISCA, volume 39)

Neurodevices for the management of nervous system disorders have been recognized as most promising through the coming decades. Technological development is being spurred on as drugs and other standard therapies have reached diminishing returns. New data enabled by cutting-edge telemetric devices will spin off new business models, for example, in seizure rhythm management. Implantable neurostimulation devices already exist as adjunct therapy for intractable epilepsy, but paradoxically, age-old feedback control strategies remain largely unknown or underutilized in the field. In this chapter we outline strategies for intelligent feedback control of pathological oscillations. We review the state of the art in implant-able devices for epilepsy and the experimental evidence for improved performance via feedback control. Then we extend an existing body of work from open-loop to continuous feedback control of phase-based models of hypersynchronization. Conversion of the results to practical devices is explored via pseudostate vector reconstruction. We conclude by outlining key components of research for continued progress in this field.


Feedback Control Deep Brain Stimulation Genetic Programming Model Predictive Control Phase Reset 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Ward and M. Rise, Techniques for treating epilepsy by brain stimulation and drug infusion, U.S. Patent 5,713,923, March 13, 1996.Google Scholar
  2. 2.
    R. Fischell, D. Fischell and A. Upton, System for treatment of neurological disorders, U.S. Patent 6,016,449, October 27, 1997.Google Scholar
  3. 3.
    J. Zabara, Neurocybernetic prosthesis, U.S. Patent 4,702,254, December 30, 1985.Google Scholar
  4. 4.
    G. Worrell, R. Wharen, R. Goodman et al., Safety and evidence for efficacy of an implantable responsive neurostimulator (RNS®) for the treatment of medically intractable partial onset epilepsy in adults, Epilepsia 46(s8), 226, 2005.CrossRefGoogle Scholar
  5. 5.
    P. Betterton, personal communication, 2005.Google Scholar
  6. 6.
    PA. Tass, Phase Resetting in Medicine and Biology: Stochastic Modelling and Data Analysis, Springer-Verlag, Berlin, 1999.zbMATHGoogle Scholar
  7. 7.
    PA. Tass, Desynchronizing double-pulse phase resetting and application to deep brain stimulation, Biological Cybernetics 85, 343–354, 2001.CrossRefGoogle Scholar
  8. 8.
    PA. Tass, Effective desynchronization with bipolar double-pulse stimulation, Physics Review E 66, 036226, 2002.CrossRefGoogle Scholar
  9. 9.
    PA. Tass, A model of desynchronizing deep brain stimulation with a demand-controlled coordinated reset of neural subpopulations, Biological Cybernetics 89, 81–88, 2003.zbMATHCrossRefGoogle Scholar
  10. 10.
    PA. Tass and M. Majtanik, Long-term anti-kindling effects of desynchronizing brain stimulation: A theoretical study, Biological Cybernetics 94(1), 58–66, 2006.zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    S. Liss, Apparatus for monitoring and counteracting excess brain electrical energy to prevent epileptic seizures and the like, U.S. Patent 3,850,161, April 9, 1973.Google Scholar
  12. 12.
    S.A. Chkhenkeli, Direct deep brain stimulation: A first step towards the feedback control of seizures, in Epilepsy as a Dynamic Disease, J. Milton and P. Jung (Eds.), Springer-Verlag, Berlin, 2003.Google Scholar
  13. 13.
    M. Nakagawa and D. Durand, Suppression of spontaneous epileptiform activity with applied currents, Brain Research 567(2), 241–247, 1991.CrossRefGoogle Scholar
  14. 14.
    S.J. Schiff, K. Jerger, D.H. Duong et al., Controlling chaos in the brain, Nature 370, 615–620, 1994.CrossRefGoogle Scholar
  15. 15.
    J. Lian, J. Shuai and D. Durand, Control of phase synchronization of neuronal activity in the rat hippocampus, Journal of Neural Engineering 1, 46–54, 2004.CrossRefGoogle Scholar
  16. 16.
    B.J. Gluckman, H. Nguyen, S.L. Weinstein et al., Adaptive electric field control of epileptic seizures, The Journal of Neuroscience 21(2), 590–600, 2001.Google Scholar
  17. 17.
    D.J. Mogul, Y. Li and M.E. Colpan, Using electrical stimulation and control feedback to modulate seizure activity in rat hippocampus, Epilepsia 46(s8), 331, 2005.Google Scholar
  18. 18.
    M. Rosenblum and A. Pikovsky, Delayed feedback control of collective synchrony: An approach to suppression of pathological brain rhythms, Physical Review E 70(041904), 1–11, 2004.Google Scholar
  19. 19.
    K. Tsakalis, Prediction and control of epileptic seizures: Coupled oscillator models. Presentation, February 2005.Google Scholar
  20. 20.
    M.W. Slutzky, P. Cvitanovic and D.J. Mogul, Manipulating epileptiform bursting in the rat hippocampus using chaos control and adaptive techniques, IEEE Transactions on Biomedical Engineering 5(5), 559–570, 2003.CrossRefGoogle Scholar
  21. 21.
    R. Larter, R. Worth and B. Speelman, Nonlinear dynamics in biochemical and biophysical systems: From enzyme kinetics to epilepsy, in Self-Organized Biological Dynamics and Nonlinear Control: Toward Understanding Complexity, Chaos and Emergent Function in Living Systems, J. Walleczek (Ed.), Cambridge University Press, Port Chester, NY, p. 51, 2001.Google Scholar
  22. 22.
    J.S. Ebersole and J. Milton, The electroencephalogram (EEG): A measure of neural synchrony, in Epilepsy as a Dynamic Disease, Springer-Verlag, Berlin, 2003.Google Scholar
  23. 23.
    J. Jefferys, Models and mechanisms of experimental epilepsies, Epilepsia 44(s12), 44–50, 2003.CrossRefGoogle Scholar
  24. 24.
    P. Suffczyn'ski, S. Kalitzin and F. Lopes da Silva, Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network, Neuroscience 126(2), 467–484, 2004.CrossRefGoogle Scholar
  25. 25.
    P. Suffczyn'ski, S. Kalitzin and F. Lopes da Silva, A lumped model of thalamic oscillations, in Proceedings of Computational Neuroscience Meeting, Brugge, Belgium, 2000.Google Scholar
  26. 26.
    F. Wendling, F. Bartolomei, J.J. Bellanger et al., Epileptic fast activities can be explained by a model of impaired GABAergic dendritic inhibition, European Journal of Neuroscience 15(9), 1499–1508, 2002.CrossRefGoogle Scholar
  27. 27.
    C. Hauptmann, O. Popovych and P. Tass, Effectively desynchronizing deep brain stimulation based on a coordinated delayed feedback stimulation via several sites: A computational study, Biological Cybernetics 93(6), 463–470, 2005.zbMATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    H. Haken, Advanced Synergetics, Springer, Berlin, 1983.zbMATHGoogle Scholar
  29. 29.
    L.G. Bleris et al., Towards embedded model predictive control for system-on-a-chip applications, Journal of Process Control 16, 255–264, 2006.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. 2009

Authors and Affiliations

  • Javier Echauz
    • 1
  • Hiram Firpi
    • 2
  • George Georgoulas
    • 3
  1. 1.Senior Research Scientist and PresidentJE Research, Inc.AlpharettaUSA
  2. 2.Department of Internal MedicineIowa CityUSA
  3. 3.TEI of the Ionian Islands, Computer Technology Applications in Management & EconomicsLefkadaGreece

Personalised recommendations