P300 Based Brain Computer Interfaces: A Progress Report

  • Emanuel Donchin
  • Yael Arbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)


Brain-Computer Interfaces (BCI) are the only means of communication available to patients who are locked-in, that is for patients who are completely paralyzed yet are fully conscious. We focus on the status of the P300-BCI first described by Farwell and Donchin (1988). This system has now been tested with several dozen ALS patients and some have been using this approach for communication at a very extensive level. More recently, we have adapted this BCI (in collaboration with the laboratory of Dr. Rajiv Dubey) to the control of a robotic arm. In this presentation we will discuss the special problems of human computer interaction that occur within the context of such a BCI. The special needs of the users forced the development of variants of this system, each with advantages and disadvantages. The general principles that can be derived from the experience we have had with this BCI will be reviewed.


Brain Computer Interface (BCI) P300 wheelchair-mounted robotic arm (WMRA) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Farwell, L., Donchin, E.: Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electro-encephalography and Clinical Neurophysiology, 510–523 (1988)Google Scholar
  2. 2.
    Sutton, S., Braren, M., Zublin, J., John, E.: Evoked Potential Correlates of Stimulus Uncertainty. Science 150, 1187–1188 (1965)CrossRefPubMedGoogle Scholar
  3. 3.
    Donchin, E., Ritter, W., McCallum, C.: Cognitive psychophysiology: The endogenous components of the ERP. In: Callaway, E., Tueting, P., Koslow, S. (eds.) Brain event-related potentials in man, pp. 349–441. Academic Press, New York (1978)CrossRefGoogle Scholar
  4. 4.
    Sellers, E.W., Donchin, E.: A P300-based brain-computer interface: Initial tests by ALS patients. Clinical Neurophysiology, 538–548 (2006)Google Scholar
  5. 5.
    Nijboer, F., Sellers, E.W., Mellinger, J., Jordon, M.A., Matuz, T., Furdea, A., Halder, S., Mochty, U., Krusienski, D.J., Vaughan, T.M., Wolpaw, J.R., Birbaumer, N., Kübler, A.: A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology 119(8), 1909–1916 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Donchin, E., Spencer, K., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil. Eng. 8, 174–179 (2000)CrossRefPubMedGoogle Scholar
  7. 7.
    Hinterberger, T., Kübler, A., Kaiser, J., Neumann, N., Birbaumer, N.A.: Brain-computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device. Clinical Neurophysiology 114, 416–425 (2003)CrossRefPubMedGoogle Scholar
  8. 8.
    Obermaier, B., Müller, G.R., Pfurtscheller, G.: ’Virtual Keyboard’ Controlled by Spontaneous EEG Activity. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(4), 422–426 (2003)CrossRefPubMedGoogle Scholar
  9. 9.
    Bell, C.J., Shenoy, P., Chalodhorn, R., Rao, R.P.N.: An Image-based Brain-Computer Interface Using the P3-Response. In: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, pp. 318–321 (2007)Google Scholar
  10. 10.
    Hinic, V., Petriu, E.M., Whalen, T.E.: Human-Computer Symbiotic Cooperation in Robot-Sensor Networks. In: Instrumentation and Measurement Technology Conference – IMTC, pp. 1–5 (2007)Google Scholar
  11. 11.
    Rebsamen, B., Burdet, E., Guan, C., Teo, C.L., Zeng, Q., Ang, M., Laugier, C.: Controlling a wheelchair using a BCI with low information transfer rate. In: Proceedings of the 2007 IEEE International Conference on Rehabilitation Robotics, pp. 1003–1008 (2007)Google Scholar
  12. 12.
    Millan, J.D.R., Renkens, F., Mourino, J., Gerstner, W.: Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG. IEEE Transactions on Biomedical Engineering 51(6), 1026–1033 (2004)CrossRefGoogle Scholar
  13. 13.
    Tanaka, K., Matsunaga, K., Wang, H.O.: Electroencephalogram-Based Control of an Electric Wheelchair. IEEE Transactions on Robotics 21(4), 762–766 (2005)CrossRefGoogle Scholar
  14. 14.
    Philips, J., Del, R., Millan, J., Vanacker, G., Lew, E., Galan, F., Ferrez, P.W., Van Brussel, H., Nuttin, M.: Adaptive Shared Control of a Brain-Actuated Simulated Wheelchair. In: Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 408–414 (2007)Google Scholar
  15. 15.
    Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R.: EEG-based neuroprosthesis control: A step towards clinical practice. Neuroscience Letters 382, 169–174 (2005)CrossRefPubMedGoogle Scholar
  16. 16.
    Lüth, T., Ojdanic, D., Friman, O., Gräser, A.: Low level control in a semi-autonomous rehabilitation robotic system via a Brain-Computer Interface. In: Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 721–728 (2007)Google Scholar
  17. 17.
    Alqasemi, R., Dubey, R.: Maximizing Manipulation Capabilities for People with Disabilities Using a 9-DoF Wheelchair-Mounted Robotic Arm System. In: Proc. of the 2007 International Conf. on Rehabilitation Robotics, Noordwijk, Netherlands (2007)Google Scholar
  18. 18.
    Arbel, Y., Alqasemi, R., Dubey, R., Donchin, E.: Adapting the P300-Brain Computer Interface (BCI) for the control of a wheelchair-mounted robotic arm system. Psychophysiology 44, S1, S82–S83 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Emanuel Donchin
    • 1
  • Yael Arbel
    • 1
  1. 1.Department of PsychologyUniversity of South FloridaTampaUSA

Personalised recommendations