Abstract

In this paper an attempt has been made to take a look at how the use of implant and electrode technology can now be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking the human brain directly with a computer. An overview of some of the latest developments in the field of Brain to Computer Interfacing is also given in order to assess advantages and disadvantages. The emphasis is clearly placed on practical studies that have been and are being undertaken and reported on, as opposed to those speculated, simulated or proposed as future projects. Related areas are discussed briefly only in the context of their contribution to the studies being undertaken. The area of focus is notably the use of invasive implant technology, where a connection is made directly with the cerebral cortex and/or nervous system.

Tests and experimentation which do not involve human subjects are invariably carried out a priori to indicate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies from this area are discussed including our own involving neural growth. The paper goes on to describe human experimentation, in which neural implants have linked the human nervous system bi-directionally with technology and the internet. A view is taken as to the prospects for the future for this implantable computing in terms of both therapy and enhancement.

Keywords

Brain-Computer Interface Biological systems Implant technology Feedback control 

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References

  1. 1.
    Branner, A., Normann, R.: A multielectrode array for intrafascicular recording and stimulation in the sciatic nerve of a cat. Brain Research Bulletin 51, 293–306 (2000)CrossRefPubMedGoogle Scholar
  2. 2.
    Chapin, J.K.: Using multi-neuron population recordings for neural prosthetics. Nature Neuroscience 7, 452–454 (2004)CrossRefPubMedGoogle Scholar
  3. 3.
    Carmena, J., Lebedev, M., Crist, R., O’Doherty, J., Santucci, D., Dimitrov, D., Patil, P., Henriquez, C., Nicolelis, M.: Learning to control a brain-machine interface for reaching and grasping by primates. Plos Biology 1(2), article number e2 (2003)Google Scholar
  4. 4.
    Dobelle, W.: Artificial vision for the blind by connecting a television camera to the visual cortex. ASAIO J. 46, 3–9 (2000)CrossRefPubMedGoogle Scholar
  5. 5.
    Donoghue, J.: Connecting cortex to machines: recent advances in brain interfaces. Nature Neuroscience Supplement 5, 1085–1088 (2002)CrossRefGoogle Scholar
  6. 6.
    Donoghue, J., Nurmikko, A., Friehs, G., Black, M.: Advances in Clinical Neurophysiology, Supplements to Clinical Neurophysiology. In: Development of a neuromotor prosthesis for humans, ch. 63, vol. 57, pp. 588–602 (2004)Google Scholar
  7. 7.
    Finn, W., LoPresti, P. (eds.): Handbook of Neuroprosthetic methods. CRC Press, Boca Raton (2003)Google Scholar
  8. 8.
    Friehs, G., Zerris, V., Ojakangas, C., Fellows, M., Donoghue, J.: Brain-machine and brain-computer interfaces. Stroke 35(11), 2702–2705 (2004)CrossRefPubMedGoogle Scholar
  9. 9.
    Gasson, M., Hutt, B., Goodhew, I., Kyberd, P., Warwick, K.: Invasive neural prosthesis for neural signal detection and nerve stimulation. Proc. International Journal of Adaptive Control and Signal Processing 19(5), 365–375 (2005)Google Scholar
  10. 10.
    Gasson, M., Wang, S., Aziz, T., Stein, J., Warwick, K.: Towards a demand driven deep brain stimulator for the treatment of movement disorders. In: Proc. 3rd IEE International Seminar on Medical Applications of Signal Processing, 16/1–16/4 (2005)Google Scholar
  11. 11.
    Grill, W., Kirsch, R.: Neuroprosthetic applications of electrical stimulation. Assistive Technology 12(1), 6–16 (2000)CrossRefPubMedGoogle Scholar
  12. 12.
    Hinterberger, T., Veit, R., Wilhelm, B., Weiscopf, N., Vatine, J., Birbaumer, N.: Neuronal mechanisms underlying control of a brain-computer interface. European Journal of Neuroscience 21(11), 3169–3181 (2005)CrossRefPubMedGoogle Scholar
  13. 13.
    Kennedy, P., Bakay, R., Moore, M., Adams, K., Goldwaith, J.: Direct control of a computer from the human central nervous system. IEEE Transactions on Rehabilitation Engineering 8, 198–202 (2000)CrossRefPubMedGoogle Scholar
  14. 14.
    Kennedy, P., Andreasen, D., Ehirim, P., King, B., Kirby, T., Mao, H., Moore, M.: Using human extra-cortical local field potentials to control a switch. Journal of Neural Engineering 1(2), 72–77 (2004)CrossRefPubMedGoogle Scholar
  15. 15.
    Mann, S.: Wearable Computing: A first step towards personal imaging. Computer 30(2), 25–32 (1997)CrossRefGoogle Scholar
  16. 16.
    Nicolelis, M., Dimitrov, D., Carmena, J., Crist, R., Lehew, G., Kralik, J., Wise, S.: Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. National Academy of the USA 100(19), 11041–11046 (2003)CrossRefGoogle Scholar
  17. 17.
    Penny, W., Roberts, S., Curran, E., Stokes, M.: EEG-based communication: A pattern recognition approach. IEEE Transactions on Rehabilitation Engineering 8(2), 214–215 (2000)CrossRefPubMedGoogle Scholar
  18. 18.
    Pinter, M., Murg, M., Alesch, F., Freundl, B., Helscher, R., Binder, H.: Does deep brain stimulation of the nucleus ventralis intermedius affect postural control and locomotion in Parkinson’s disease? Movement Disorders 14(6), 958–963 (1999)CrossRefPubMedGoogle Scholar
  19. 19.
    Reger, B., Fleming, K., Sanguineti, V., Simon Alford, S., Mussa-Ivaldi, F.: Connecting Brains to Robots: an artificial body for studying computational properties of neural tissues. Artificial life 6(4), 307–324 (2000)CrossRefPubMedGoogle Scholar
  20. 20.
    Rizzo, J., Wyatt, J., Humayun, M., DeJuan, E., Liu, W., Chow, A., Eckmiller, R., Zrenner, E., Yagi, T., Abrams, G.: Retinal Prosthesis: An encouraging first decade with major challenges ahead. Opthalmology 108(1) (2001)Google Scholar
  21. 21.
    Roitberg, B.: Noninvasive brain-computer interface. Surgical Neurology 63(3), 195 (2005)CrossRefGoogle Scholar
  22. 22.
    Warwick, K.: I Cyborg. University of Illinois Press (2004)Google Scholar
  23. 23.
    Warwick, K., Gasson, M., Hutt, B., Goodhew, I., Kyberd, P., Andrews, B., Teddy, P., Shad, A.: The application of implant technology for cybernetic systems. Archives of Neurology 60(10), 1369–1373 (2003)CrossRefPubMedGoogle Scholar
  24. 24.
    Warwick, K., Gasson, M., Hutt, B., Goodhew, I., Kyberd, P., Schulzrinne, H., Wu, X.: Thought Communication and Control: A First Step Using Radiotelegraphy. IEE Proceedings on Communications 151(3), 185–189 (2004)CrossRefGoogle Scholar
  25. 25.
    Warwick, K., Gasson, M., Hutt, B., Goodhew, I.: An Attempt to Extend Human Sensory Capabilities by means of Implant Technology. In: Proc. IEEE Int. Conference on Systems, Man and Cybernetics, Hawaii (2005)Google Scholar
  26. 26.
    Wolpaw, J., McFarland, D., Neat, G., Forheris, C.: An EEG based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78, 252–259 (1990)CrossRefGoogle Scholar
  27. 27.
    Pan, S., Warwick, K., Gasson, M., Burgess, J., Wang, S., Aziz, T., Stein, J.: Prediction of parkinson’s disease tremor onset with artificial neural networks. In: Proc. IASTED Conference BioMed 2007, Innsbruck, Austria, pp. 341–345 (2007)Google Scholar
  28. 28.
    Warwick, K.: The promise and threat of modern cybernetics. Southern Medical Journal 100(1), 112–115 (2007)CrossRefPubMedGoogle Scholar
  29. 29.
    Warwick, K., Gasson, M.N.: Practical Interface Experiments with Implant Technology. In: Sebe, N., Lew, M., Huang, T.S. (eds.) ECCV/HCI 2004. LNCS, vol. 3058, pp. 7–16. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  30. 30.
    Yoo, S., Fairneny, T., Chen, N., Choo, S., Panych, L., Park, H., Lee, S., Jolesz, F.: Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport 15(10), 1591–1595 (2004)CrossRefPubMedGoogle Scholar
  31. 31.
    Yu, N., Chen, J., Ju, M.: Closed-Loop Control of Quadriceps/Hamstring activation for FES-Induced Standing-Up Movement of Paraplegics. Journal of Musculoskeletal Research 5(3), 173–184 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kevin Warwick
    • 1
  1. 1.University of ReadingU.K.

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