Toward a Wearable, Neurally-Enhanced Augmented Reality System

  • David H. Goldberg
  • R. Jacob Vogelstein
  • Diego A. Socolinsky
  • Lawrence B. Wolff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6780)

Abstract

Augmented reality systems hold great promise, but as they become more complex they can become more challenging to use. Incorporating neural interfaces into augmented reality systems can dramatically increase usability and utility. We explore these issues in the context of Equinox Corporation’s Night REAPERTM system-an augmented reality system for dismounted warfighters. We describe the current Night REAPER system and then survey some of the potential enhancements and unique design challenges associated with the addition of a neural interface. Signals, sensors, and decoding techniques for the system’s brain-machine interface are discussed.

Keywords

augmented reality brain-machine interface wearable systems 

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References

  1. 1.
    Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering 4(2), 32–57 (2007)CrossRefGoogle Scholar
  2. 2.
    Boland, R.: Army uses advanced systems to understand what soldiers know. Signal Magazine (March 2008), http://www.afcea.org/signal/articles/templates/Signal_Article_Template.asp?articleid=1528&zoneid=228
  3. 3.
    Department of the Army, Washington, D.C.: U.S. Army Field Manual 21-18: Foot Marches (1990)Google Scholar
  4. 4.
    van Gerven, M., Farquhar, J., Schaefer, R., Vlek, R., Geuze, J., Nijholt, A., Ramsey, N., Haselager, P., Vuurpijl, L., Gielen, S., Desain, P.: The brain-computer interface cycle. Journal of Neural Engineering 6(4), 41001 (2009)CrossRefGoogle Scholar
  5. 5.
    Krusienski, D.J., Sellers, E.W., Cabestaing, F., Bayoudh, S., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: A comparison of classification techniques for the P300 speller. Journal of Neural Engineering 3(4), 299 (2006)CrossRefGoogle Scholar
  6. 6.
    Medvedev, A.V., Kainerstorfer, J., Borisov, S.V., Barbour, R.L., VanMeter, J.: Event-related fast optical signal in a rapid object recognition task: Improving detection by the independent component analysis. Brain Research 1236, 145–158 (2008)CrossRefGoogle Scholar
  7. 7.
    Melzer, J.E., Brozoski, F.T., Letowski, T.R., Harding, T.H., Rash, C.E.: Guidelines for HMD design. In: Rash, C.E., Russo, M.B., Letowski, T.R., Schmeisser, E.T. (eds.) Helmet-Mounted Displays: Sensation, Perception and Cognition Issues, pp. 805–848. U.S. Army Aeromedical Research Laboratory, Fort Rucker (2009), http://www.usaarl.army.mil/new/publications/HMD_Book09/ Google Scholar
  8. 8.
    U.S. Army PEO Soldier: Advanced combat helmet (ACH), https://peosoldier.army.mil/factsheets/SEQ_SSV_ACH.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • David H. Goldberg
    • 1
  • R. Jacob Vogelstein
    • 2
  • Diego A. Socolinsky
    • 3
  • Lawrence B. Wolff
    • 1
  1. 1.Equinox CorporationNew YorkUSA
  2. 2.Applied Physics LaboratoryJohns Hopkins UniversityLaurelUSA
  3. 3.Equinox CorporationBaltimoreUSA

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