Advertisement

Motor Priming as a Brain-Computer Interface

  • Tom Stewart
  • Kiyoshi Hoshino
  • Andrzej Cichocki
  • Tomasz M. Rutkowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9948)

Abstract

This paper reports on a project to overcome a difficulty associated with motor imagery (MI) in a brain–computer interface (BCI), in which user training relies on discovering how to best carry out the MI given only open-ended instructions. To address this challenge we investigate the use of a motor priming (MP), a similar mental task but one linked to a tangible behavioural goal. To investigate the efficacy of this approach in creating the changes in brain activity necessary to drive a BCI, an experiment is carried out in which the user is required to prepare and execute predefined movements. Significant lateralisations of alpha activity are discussed and significant classification accuracies of movement preparation versus no preparation are also reported; indicating that this method is promising alternative to motor imagery in driving a BCI.

Keywords

Brain-computer interface (BCI) Motor priming EEG Neurophysiological information processing and classification 

References

  1. 1.
    Deiber, M.P., Sallard, E., Ludwig, C., Ghezzi, C., Barral, J., Ibañez, V.: EEG alpha activity reflects motor preparation rather than the mode of action selection. Front. Integrat. Neurosci. 6(59) (2012)Google Scholar
  2. 2.
    LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., He, B.: Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. J. Neural Eng. 10(4), 046003 (2013)CrossRefGoogle Scholar
  3. 3.
    Lotte, F., Larrue, F., Mühl, C.: Flaws in current human training protocols for spontaneous brain-computer interfaces: lessons learned from instructional design. Front. Hum. Neurosci. 7, 568 (2013)CrossRefGoogle Scholar
  4. 4.
    Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.M.: Fieldtrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, Article no. 1 (2010)Google Scholar
  5. 5.
    Pfurtscheller, G., Brunner, C., Schlögl, A., da Silva, L.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31(1), 153–159 (2006)CrossRefGoogle Scholar
  6. 6.
    Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)CrossRefGoogle Scholar
  7. 7.
    Rushworth, M.F., Krams, M., Passingham, R.E.: The attentional role of the left parietal cortex: the distinct lateralization and localization of motor attention in the human brain. J. Cogn. Neurosci. 13(5), 698–710 (2001)CrossRefGoogle Scholar
  8. 8.
    Scherer, R., Lee, F., Schlogl, A., Leeb, R., Bischof, H., Pfurtscheller, G.: Toward self-paced brain-computer communication: navigation through virtual worlds. IEEE Trans. Biomed. Eng. 55(2), 675–682 (2008)CrossRefGoogle Scholar
  9. 9.
    Van Gerven, M., Bahramisharif, A., Heskes, T., Jensen, O.: Selecting features for BCI control based on a covert spatial attention paradigm. Neural Netw. 22(9), 1271–1277 (2009)CrossRefGoogle Scholar
  10. 10.
    Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, Oxford (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Tom Stewart
    • 1
    • 2
  • Kiyoshi Hoshino
    • 2
  • Andrzej Cichocki
    • 1
  • Tomasz M. Rutkowski
    • 1
    • 3
    • 4
  1. 1.RIKEN Brain Science InstituteWako-shiJapan
  2. 2.University of TsukubaTsukubaJapan
  3. 3.The University of TokyoTokyoJapan
  4. 4.Saitama Institute of TechnologySaitamaJapan

Personalised recommendations