The Potential of the BCI for Accessible and Smart e-Learning

  • Ray Adams
  • Richard Comley
  • Mahbobeh Ghoreyshi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5615)


The brain computer interface (BCI) should be the accessibility solution “par excellence” for interactive and e-learning systems. There is a substantial tradition of research on the human electro encephalogram (EEG) and on BCI systems that are based, inter alia, on EEG measurement. We have not yet seen a viable BCI for e-learning. For many users for a BCI based interface is their first choice for good quality interaction, such as those with major psychomotor or cognitive impairments. However, there are many more for whom the BCI would be an attractive option given an acceptable learning overhead, including less severe disabilities and safety critical conditions where cognitive overload or limited responses are likely. Recent progress has been modest as there are many technical and accessibility problems to overcome. We present these issues and report a survey of fifty papers to capture the state-of-the-art in BCI and the implications for e-learning.


brain-computer-interface e-learning accessibility disability artifacts 


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  1. 1.
    Adams, R., Granić, A.: Creating Smart and Accessible Ubiquitous Knowledge Environments. In: Stephanidis, C. (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 3–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Adams, R., Bahr, G.S., Moreno, B.: Brain Computer Interfaces: Psychology and Pragmatic Perspectives for the Future. In: Annual convention of the Artificial Intelligence and Simulation of Behaviour (AISB) Society (2008)Google Scholar
  3. 3.
    Berger, H.: Uber des Eleklrenkephalogramm des Menschen. Arch. Psychiat., 16–60 (1929)Google Scholar
  4. 4.
    Borkotoky, C., Swapnil Galgate, S., Nimbekar, S.B.: Human Computer Interaction: Harnessing P300 Potential brain waves for Authentication of Individuals. In: Compute 2008, Bangalore, Karnataka, India, January 18-20, pp. 1–4 (2008)Google Scholar
  5. 5.
    Felzer, T., Ernst, M., Strah, B., Nordmann, R.: Accessibility Research at the Department of Mechatronics at Darmstadt University of Technology. Sigaccess Newsletter (88), 19–28 (2007)Google Scholar
  6. 6.
    Jung, T.-P., Makeig, S., Humphries, C., Lee, T.-W., McKeown, M., Iragui, V., Sejnowski, T.: Removing electroencephalographic artifacts by blind source separation. In: Psychophysiology, pp. 163–178. Cambridge University Press, Cambridge (2000)Google Scholar
  7. 7.
    Lawrence, S., Giles, C.L.: Accessibility of information on the web. Nature 400, 107 (1999)CrossRefGoogle Scholar
  8. 8.
    Lebdev, M.A., Nicolelis, M.A.L.: Brain-Machine Interfaces: past, present and future. Trends in Neurosci. 29(9), 536–546 (2006)CrossRefGoogle Scholar
  9. 9.
    Nielsen, J.: Usability engineering. Morgan Kaufmann, N.Y. (1994)zbMATHGoogle Scholar
  10. 10.
    Popescu, F., Badower, Y., Fazli, S., Dornhege, G., Muller, K.-R.: EEG-based control of reaching to visual targets. In: Dynamical Principles for neuroscience and intelligent biomimetic devices - Abstracts of the EPFL-LATSIS Symposium 2006, pp. 123–124, 1–2 (2006)Google Scholar
  11. 11.
    Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley-Interscience, London (2007)CrossRefGoogle Scholar
  12. 12.
    Stowell, H.: No future in the averaged scalp. Nature, 1074 (1970)Google Scholar
  13. 13.
    Szykman, S., Racz, J.W., Sriram, R.D.: The representation of function in computer-based design. In: Proceedings of the 1999 ASME Design Engineering Technical Conferences, Las Vegas, Nevada, September 12-15 (1999) DETC99/DTM-8742 Google Scholar
  14. 14.
    Teplan, M.: Fundamentals of EEG Measurements. Measurement Science Review 2(2) (2002)Google Scholar
  15. 15.
    Walter, W.G.: The location of cerebral tumours by electroencephalography. The Lancet, 305–308 (1936)Google Scholar
  16. 16.
    Wills, S.A., Mackay, D.J.C.: DASHER – An efficient writing system for Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14, 244–246 (2006)CrossRefGoogle Scholar
  17. 17.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, J., Vaughan, T.M.: Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering [also IEEE Trans. on Neural Systems and Rehabilitation 8, 164] (2000)Google Scholar
  18. 18.
    Zhao, Q., Zhang, L.: Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface. Hindawi Publishing Corporation, Computational Intelligence and Neuroscience (2007) Article ID 37695 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ray Adams
    • 1
  • Richard Comley
    • 1
  • Mahbobeh Ghoreyshi
    • 1
  1. 1.School of Engineering & Information SciencesMiddlesex University The Burroughs, HendonLondonUK

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