Advertisement

Using Behavioral Information to Contextualize BCI Performance

  • Stephen M. GordonEmail author
  • Jonathan R. McDaniel
  • Jason S. Metcalfe
  • Antony D. Passaro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9183)

Abstract

Brain-computer interface (BCI) systems often require millisecond-level timing precision in order to function reliably. However, as BCI research expands to an ever-widening array of applications, including operation in real-world environments, such timing requirements will need to be relaxed. In addition, overall BCI system design must be improved in order to better disambiguate the numerous, seemingly similar, neural responses that may arise in such environments. We argue that this new area of operational BCI will require the integration of neural data with non-neural contextual variables in order to function reliably. We propose a framework in which non-neural contextual information can be used to better scope the operational BCI problem by indicating windows of time for specific analyses as well as defining probability distributions over these windows. We demonstrate the utility of our framework on a sample data set and provide discussion on many of the factors influencing performance.

Keywords

Brain-computer interface (BCI) Electroencephalography (EEG) Asynchronous event detection 

Notes

Acknowledgements

The authors would like to thank Dr. Kelvin Oie of the Army Research Laboratory for his help in designing the research study. This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-2-0022. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

References

  1. 1.
    Lin, C.-T., Wu, R.-C., Liang, S.-F., Chao, W.-H., Chen, Y.-J., Jung, T.-P.: EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Trans. Circuits Syst. I Regul. Pap. 52(12), 2726–2738 (2005)CrossRefGoogle Scholar
  2. 2.
    Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(Supplement 1), B231–B244 (2007)Google Scholar
  3. 3.
    McDowell, K., Lin, C.-T., Oie, K.S., Jung, T.-P., Gordon, S., Whitaker, K.W., Li, S.-Y., Lu, S.-W., Hairston, W.D.: Real-world neuroimaging technologies. IEEE Access 1, 131–149 (2013)CrossRefGoogle Scholar
  4. 4.
    Makeig, S., Gramann, K., Jung, T.-P., Sejnowski, T.J., Poizner, H.: Linking brain, mind and behavior. Int. J. Psychophysiol. 73(2), 95–100 (2009)CrossRefGoogle Scholar
  5. 5.
    Leeb, R., Friedman, D., Müller-Putz, G.R., Scherer, R., Slater, M., Pfurtscheller, G.: Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput. Intell. Neurosci. 2007, 79642 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
    Townsend, G., Graimann, B., Pfurtscheller, G.: Continuous EEG classification during motor imagery-simulation of an asynchronous BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 12(2), 258–265 (2004)CrossRefzbMATHGoogle Scholar
  7. 7.
    Marathe, A.R., Anthony, J.R., McDowell, K.: Sliding HDCA: single-trial EEG classification to overcome and quantify temporal variability. IEEE Trans. Neural Syst. Rehabil. Eng. 22, 201–211 (2014)CrossRefGoogle Scholar
  8. 8.
    Schapire, R.E.: The boosting approach to machine learning: an overview. In: Denison, D.E., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds.) Nonlinear Estimation and Classification. Lecture Notes in Statistics, pp. 149–171. Springer, New York (2003)CrossRefGoogle Scholar
  9. 9.
    Makeig, S., Bell, A.J., Jung, T.-P., Sejnowski, T.J.: Independent component analysis of electroencephalographic data. In: Advances in Neural Information Processing Systems, pp. 145–151 (1996)Google Scholar
  10. 10.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stephen M. Gordon
    • 1
    Email author
  • Jonathan R. McDaniel
    • 1
  • Jason S. Metcalfe
    • 1
  • Antony D. Passaro
    • 1
  1. 1.DCS CorporationAlexandriaUSA

Personalised recommendations