Advertisement

An Exploration of the Utilization of Electroencephalography and Neural Nets to Control Robots

  • Dan Szafir
  • Robert Signorile
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6949)

Abstract

It has long been known that as neurons fire within the brain they produce measurable electrical activity. Electroencephalography (EEG) is the measurement and recording of these electrical signals using sensors arrayed across the scalp. The idea of Brain-Computer interfaces (BCIs), which allow the control of devices using brain signals, naturally present themselves to many extremely useful applications including prosthetic devices, restoring or aiding in communication and hearing, military applications, video gaming and virtual reality, and robotic control, and have the possibility of significantly improving the quality of life of many disabled individuals. The purpose of this research is to examine an off the shelf EEG system, the Emotiv EPOC© System, as a cost-effective gateway to non-invasive portable EEG measurements and to build a BCI to control a robot, the Parallax Scribbler®. We built middleware to interpret the outputs from the Emotiv and map them into commands for the Scribbler robot.

Keywords

Human-Robot Interaction Computer Human Interface Control Systems Neural networks 

References

  1. 1.
    Swartz, B.E., Goldensohn, E.: Timeline of the history of EEG and associated fields. Electroencephalography and Clinical Neurophysiology 106, 173–176 (1998)CrossRefGoogle Scholar
  2. 2.
    Millett, D.: Hans Berger: from psychic energy to the EEG. Perspectives in Biology and Medicine 44(4), 522–542 (2001)CrossRefGoogle Scholar
  3. 3.
    Nunez, P.L., Srinivasan, R.: Electric Fields of the Brain: The Neurophysics of EEG. Oxford University Press, Oxford (1981)Google Scholar
  4. 4.
    Niedermeyer, E., da Silva, F.L.: Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 5th edn., p. 140. Lippincott Williams & Wilkins (2005)Google Scholar
  5. 5.
    Rowan, A.J.: Primer of EEG. Elsevier Science, Philadelphia (2003)Google Scholar
  6. 6.
    Ludwig, K.A., et al.: Employing a Common Average Reference to Improve Cortical Neuron Recordings from Microelectrode Arrays. Journal of Neurophysiology, September 3 (2008)Google Scholar
  7. 7.
    Vidal, J.: Toward Direct Brain–Computer Communication. Annual Review of Biophysics and Bioengineering 2, 157–180 (1973)CrossRefGoogle Scholar
  8. 8.
    Fetz, E.E.: Operant Conditioning of Cortical Unit Activity. Science 163, 955–958 (1969)CrossRefGoogle Scholar
  9. 9.
    Kennedy, P.R., et al.: Activity of single action potentials in monkey motor cortex during long-term task learning. Brain Research 760(1-2), 251–254 (1997)CrossRefGoogle Scholar
  10. 10.
    Wessber, J., et al.: Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408(6810), 361–365 (2000)CrossRefGoogle Scholar
  11. 11.
    Carey, B.: Monkeys Think, Moving Artifiacl Arm as Own. The New York Times, May 29 (2008)Google Scholar
  12. 12.
    Hochberg, L.R., et al.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006)CrossRefGoogle Scholar
  13. 13.
    Fabiani, G.E., et al.: Conversion of EEG activity into cursor movement by a brain-computer interfaceGoogle Scholar
  14. 14.
    Vidal, J.: Toward Direct Brain–Computer CommunicationGoogle Scholar
  15. 15.
    Omidvarnia, A.H., et al.: Kalman Filter Parameters as a New EEG Feature Vector for BCI ApplicationsGoogle Scholar
  16. 16.
    Niedermeyer: Electroencephalography, pp. 1265–1266Google Scholar
  17. 17.
    Sellers, E.W., et al.: A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance. Biological Psychology 73(3), 242–252 (2006)CrossRefGoogle Scholar
  18. 18.
    Adlakha, A.: Single Trial EEG Classification. Swiss Federal Institute of Technology, July 12 (2002)Google Scholar
  19. 19.
    Lu, S., et al.: Unsupervised Brain Computer Interface based on Inter-Subject Information. In: 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, August 20-24 (2008)Google Scholar
  20. 20.
    Niedermyer: Electroencephalography, p. 1240Google Scholar
  21. 21.
    Kauhanen, L., Palomaki, T., Jylanki, P., Aloise, F., Nuttin, M., Millan, J.R.: Haptic feedback compared with visual feedback for BCI. In: Kauhanen, L., Palomaki, T., Jylanki, P., Aloise, F., Nuttin, M., Millan, J. (eds.) Proceeding of 3rd International BCI Workshop and Training Course 2006, Graz, Austria, September 21-25, pp. 66–67 (2006)Google Scholar
  22. 22.
    Niedermeyer: Electroencephalography, p. 1265Google Scholar
  23. 23.
    Birbaumer, N., et al.: The Thought Translation Device (TTD) for Completely Paralyzed Patients. IEEE Transactions on Rehabilitation Engineering 8(2), 190–193 (2000)CrossRefGoogle Scholar
  24. 24.
    Galán, F., et al.: A Brain-Actuated Wheelchair: Asynchronous and Non-invasive Brain-Computer Interfaces for Continuous Control of Robots. Clinical Neurophysiology 119(9), 2159–2169 (2008)CrossRefGoogle Scholar
  25. 25.
    Drummond, K.: Pentagon Preps Soldier Telepathy Push. Wired Magazine, May 14 (2009)Google Scholar
  26. 26.
    Emotiv Website, http://www.emotiv.com
  27. 27.
    The Scribbler: A Reprogrammable Robot, http://www.parallax.com/tabid/455/Default.aspx, Copyright 2010 by Parallax Inc. (accessed November 4, 2010)
  28. 28.
    Usage Guides, http://www.roboteducation.org/guides.html, Copyright © 2007 Institute for Personal Robots in Education (accessed November 4, 2010)
  29. 29.
    Szafir, D.: Non-Invasive BCI through EEG, unpublished Undergraduate Honors Thesis in Computer Science, Boston College (2010)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Dan Szafir
    • 1
  • Robert Signorile
    • 1
  1. 1.Computer Science DepartmentBoston CollegeChestnut HillUSA

Personalised recommendations