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EEG-Based Brain-Computer Interface for Control of Assistive Devices

  • Nikolay V. KapralovEmail author
  • Jaroslav V. Ekimovskii
  • Vyacheslav V. Potekhin
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 95)

Abstract

The study describes an approach for supervisory control of a limb prosthesis and a mobile robot based on a non-invasive brain-computer interface. Key applications of the system are the maintenance of immobilized patients and rehabilitation procedures. An interface performs imaginary hand movement decoding using electroencephalographic signals. The decoding process consists of several steps: (1) signal acquisition; (2) signal preprocessing (filtering, artefact removal); (3) feature extraction; (4) classification. The study is focused on obtaining the best accuracy of decoding by comparing different feature extraction and classification methods. Several methods (Riemannian geometry-based) were tested offline. Furthermore, online testing of control capabilities using in-house data was performed.

Keywords

EEG Brain-computer interface Assistive devices 

Notes

Acknowledgements

The work was financially supported by RFBR grant 16-29-08296.

References

  1. 1.
    Achic, F., Montero, J., Penaloza, C., Cuellar, F.: Hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks. In: 2016 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO), pp. 249–254 (2016).  https://doi.org/10.1109/arso.2016.7736290
  2. 2.
    Boulcher, S.: Obstacle detection and avoidance using TurtleBot. Research Assistantship Report, Department of Computer Science, Rochester Institute of Technology, Monroe County, NY, USA, p. 6 (2012)Google Scholar
  3. 3.
    Gundelakh, F., Stankevich, L., Sonkin, K.: Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands. MATEC Web Conf. 161 03003 (2018).  https://doi.org/10.1051/matecconf/201816103003
  4. 4.
    Wolpaw, J.R., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, New York (2012)CrossRefGoogle Scholar
  5. 5.
    Congedo, M., Barachant, A., Bhatia, R.: Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain Comput. Interfaces 4(3), 155–174 (2017).  https://doi.org/10.1080/2326263X.2017.1297192CrossRefGoogle Scholar
  6. 6.
    Barachant, A., Bonnet, S., Congedo, M., Jutten, C.: Riemannian geometry applied to BCI classification. In: 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA 2010), Saint-Malo, France, pp. 629–636 (2010).  https://doi.org/10.1007/978-3-642-15995-4_78
  7. 7.
    Fox, D., Burgardy, W., Dellaert, F., Thrun, S.: Monte Carlo Localization: efficient position estimation for mobile robots. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA and Computer Science Department III University of Bonn, Bonn, Germany (1999)Google Scholar
  8. 8.
    Frese, U.: Treemap: an O (log n) algorithm for indoor simultaneous localization and mapping. Auton. Robots 21, 103–122 (2006)CrossRefGoogle Scholar
  9. 9.
    Dellaert, F., Kaess, M.: Square Root SAM: simultaneous localization and mapping via square root information smoothing. Int. J. Robot. Res. 25, 1181–1203 (2006)CrossRefGoogle Scholar
  10. 10.
    Ni, K., Steedly, D., Dellaert, F.: Tectonic SAM: exact, out-of-core, submap-based SLAM. In: 2007 IEEE International Conference on Robotics and Automation (2007)Google Scholar
  11. 11.
  12. 12.
    McDonald, S., Salimi, P.: Motion planning of intelligent robots. BSc Qualifying Project Report, Worcester Polytechnic Institute, Worcester, MA, USA, p. 20–21 (2014)Google Scholar
  13. 13.
    Cong, R., Winters, R.: How does the Xbox Kinect work. https://www.jameco.com/jameco/workshop/howitworks/xboxkinect.html
  14. 14.
    Pagliaria, D., Pinto, L., Reguzzoni, M., Rossi, L.: Integration of kinect and low-cost gnss for outdoor navigation. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLI-B5, 565–572 (2016).  https://doi.org/10.5194/isprs-archives-XLI-B5-565-2016
  15. 15.
    Decety, J., Michel, F.: Comparative analysis of actual and mental movement times in two graphic tasks. Brain Cogn. 11, 87 (1989)CrossRefGoogle Scholar
  16. 16.
    Sirigu, A., Duhamel, J.R., Cohen, L., et al.: The mental representation of hand movements after parietal cortex damage. Science 273(5281), 1564 (1996)ADSCrossRefGoogle Scholar
  17. 17.
    Neuper, C., Scherer, R., Reiner, M., Pfurtscheller, G.: Imagery of motor actions: differential effects of kinesthetic and visual motor mode of imagery in single trial EEG. Cogn. Brain. Res. 25(3), 668 (2005)CrossRefGoogle Scholar
  18. 18.
    Barachant, A., Bonnet, S., Congedo, M., Jutten, C.: BCI signal classification using a Riemannian-based kernel. In: 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges, Belgium, pp. 97–102 (2012)Google Scholar
  19. 19.
    Wang, E.-Y., Guo, W., Dai, L.-R., et al.: Factor analysis based spatial correlation modeling for speaker verification. In: ISCSLP 2010, Tainan, China, pp. 166–170 (2010)Google Scholar
  20. 20.
    Brunner, A.C., Leeb, R., Mueller-Putz, G.R., Schloegl, A., Pfurtscheller, G.: BCI Competition 2008 – Graz data set (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Nikolay V. Kapralov
    • 1
    Email author
  • Jaroslav V. Ekimovskii
    • 1
  • Vyacheslav V. Potekhin
    • 1
  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySaint PetersburgRussia

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