Advertisement

Controlling Robots Using EEG Signals, Since 1988

  • Stevo Bozinovski
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 207)

Abstract

The paper considers the emergence of the field of controlling robots using EEG signals. It looks back to the first result in the field, achieved in 1988. From a viewpoint of EEG driven control, it was the first result in controlling a physical object using EEG signals. The paper gives details of the development of the research infrastructure which enabled such a result, including description of the lab setup and algorithms. The paper also gives a description of the scientific context in which the result was achieved by giving a short overview of the first ten papers in the field of EEG driven control.

Keywords

psychokinesis EEG control of physical objects EEG control of robots biosignal processing contingent negative variation contingent alpha rhythm variation probability density distribution real-time EEG control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bozinovski, S., Bozinovska, L., Setakov, M.: Mobile robot control using alpha wave from the human brain. In: Proc. Symp. JUREMA, Zagreb, pp. 247–249 (1988) (in Croatian)Google Scholar
  2. 2.
    Bozinovski, S., Sestakov, M., Bozinovska, L.: Using EEG alpha rhythm to control a mobile robot. In: Harris, G., Walker, C. (eds.) Proc. 10th Annual Conf. of the IEEE Engineering in Medicine and Biology Society, Track 17: Biorobotics, New Orleans, vol. 3, pp. 1515–1516 (1988)Google Scholar
  3. 3.
    Bozinovski, S.: Mobile robot trajectory control: From fixed rails to direct bioelectric control. In: Kaynak, O. (ed.) Proc. IEEE International Workshop on Intelligent Motion Control, Istanbul, vol. 2, pp. 463–467 (1990)Google Scholar
  4. 4.
    Bozinovski, S., Sestakov, M., Stojanov, G.: A learning system for mobile robot control using human head biosignals. Problemji Mashinostroenija i Avtomatizacii 6, 32–35 (1991) (in Russian)Google Scholar
  5. 5.
    Walter, G., Cooper, R., Aldridge, V., McCallum, W.: Contingent Negative Variation: An electric sign of sensory-motor association and expectancy in the human brain. Nature 203, 380–384 (1964)CrossRefGoogle Scholar
  6. 6.
    Bozinovska, L.: The CNV paradigm: Electrophysiological evidence of expectation and attention. Unpublished Term Paper. Course PSYCH330 Physiological Psychology, Instructor Beth Powel, Psychology Department, University of Massachusetts/Amherst (1981)Google Scholar
  7. 7.
    Bozinovska, L., Isgum, V., Barac, B.: Electrophysiological and phenomenological evidence of the expectation process in the reaction time measurements. Yugoslavian Physiologica and Pharmacologica Acta, 21–22 (1985)Google Scholar
  8. 8.
    Bozinovska, L., Bozinovski, S., Stojanov, G.: Electroexpectogram: Experimental design and algorithms. In: Proc. IEEE International Biomedical Engineering Days, Istanbul, pp. 58–60 (1992)Google Scholar
  9. 9.
    Kornhuber, H., Deecke, L.: Changes in brain potentials in case of willing movements and passive movements in humans: Readiness potential and reaferent potentials. Pflügers Arch. 284, 1–17 (1965) (in German)CrossRefGoogle Scholar
  10. 10.
    Bozinovska, L., Sestakov, M., Stojanovski, G., Bozinovski, S.: Intensity variation of the CNV potential during the biofeedback training guided by a personal computer. Neurologija 37(suppl. 2) (1988) (in Serbian)Google Scholar
  11. 11.
    Bozinovski, S., Sestakov, M.: Multitasking operating systems and their application in robot control. In: Proc. Workshop on Macedonian Informatics, Skopje, pp. 195–199 (1983) (in Macedonian)Google Scholar
  12. 12.
    Walker, T.: Fundamentals of Cobol Programming: A Structured Approach. Allyn and Bacon (1976)Google Scholar
  13. 13.
    Keirn, Z., Aunon, J.: A new mode of communication between man and his surroundings. IEEE Transactions on Biomedical Engineering 37(12), 1209–1214 (1990)CrossRefGoogle Scholar
  14. 14.
    Dewan, E.: Occipital alpha rhythm eye position and lens accommodation. Nature 214, 975–977 (1967)CrossRefGoogle Scholar
  15. 15.
    Vidal, J.: Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering, 157–180 (1973)Google Scholar
  16. 16.
    Vidal, J.: Real-time detection of brain events in EEG. Proceedings of the IEEE 65, 633–641 (1977)CrossRefGoogle Scholar
  17. 17.
    Farwell, L., Donchin, E.: Talking off the top of your head: a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70, 510–523 (1988)CrossRefGoogle Scholar
  18. 18.
    Wolpaw, J., McFarland, D., Neat, G., Forneris, C.: An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology 78(3), 252–259 (1991)CrossRefGoogle Scholar
  19. 19.
    Sutter, E.: The brain response interface: Communication through visually induced electrical brain responses. Journal of Microcomputer Applications 15, 31–45 (1992)CrossRefGoogle Scholar
  20. 20.
    Cilliers, P., Van Der Kouwe, A.: A VEP-based computer interface for C2-quadriplegics. In: Proc. of the 15th Annual International Conf. of the IEEE, p. 1263 (1993)Google Scholar
  21. 21.
    Pfurtscheller, G., Flotzinger, D., Kalcher, J.: Brain Computer Interfaces – A new communication device for handicapped person. Journal of Microcomputer Applications 16, 293–299 (1993)CrossRefGoogle Scholar
  22. 22.
    Chapin, J., Moxon, K., Markowitz, R., Nicolelis, M.: Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nature Neuroscience 2, 664–670 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.South Carolina State UniversityOrangeburgUSA

Personalised recommendations