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Human-Robot Interaction Using Brain-Computer Interface Based on EEG Signal Decoding

  • Lev Stankevich
  • Konstantin SonkinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9812)

Abstract

This study describes a new approach to a problem of the human-robot interaction for remote control of robot behavior. Finding a solution to this problem is important for providing control of robots and unmanned vehicles. At the interaction a human operator can form commands for robot control. It is proposed to use a noninvasive brain-computer interface based on the decoding of signals of brain activity during motor imagery to generate the supervisor commands for robot control. The principles of the interaction of human as an operator and robot as an executor are considered. Using the brain-computer interface the operator can change robot behavior without any special movements and modules embedded into robot’s program. The study aimed to development of the human-robot interaction system for non-direct control of the robot behavior based on the brain-computer interface for classification of EEG patterns of imaginary movements of one hand fingers in real-time. Example of such human-robot interaction realization for Nao robot with neurofeedback is considered.

Keywords

Brain-computer interface Classifier committee Imaginary finger movements Human-robot interaction 

References

  1. 1.
    Wolpaw, J.R., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, Oxford (2012)CrossRefGoogle Scholar
  2. 2.
    Daly, I., Billinger, M., Laparra-Hernández, J., Aloise, F., García, M.L., Faller, J., Scherer, R., Müller-Putz, G.: On the control of brain-computer interfaces by users with cerebral palsy. Clin. Neurophysiol. 124, 1787–1797 (2013)CrossRefGoogle Scholar
  3. 3.
    Frolov, A.A., Roshin, V.U.: Brain computer interface. Reality and perspectives. In: Scientific Conference on Neuroinformatic MIFI 2008. Lections on Neuroinformatics (2008). http://neurolectures.narod.ru/2008/Frolov-2008.pdf (in Russian)
  4. 4.
    Kaplan, A.Y., Kochetkov, A.G., Shishkin, S.L., et al.: Experimental-theoretic bases and practical realizations of technology “Brain computer interface”. Sibir Med. Bull. 12(2), 21–29 (2013). (in Russian)Google Scholar
  5. 5.
    Bai, O., Lin, P., Vorbach, S., Floeter, M.K., Hattori, N., Hallett, M.: A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior. J. Neural Eng. 5(1), 24 (2008)CrossRefGoogle Scholar
  6. 6.
    Hsu, W.: Embedded grey relation theory in hopfield neural network application to motor imagery eeg recognition. Clin. EEG Neurosci. 44(4), 257–264 (2013)CrossRefGoogle Scholar
  7. 7.
    Huang, D., Lin, P., Fei, D.Y., Chen, X., Bai, O.: EEG-based online two-dimensional cursor control. In: Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4547–4550 (2009) Google Scholar
  8. 8.
    Leeb, R., Scherer, R., Keinrath, C., Guger, C., Pfurtscheller, G.: Exploring virtual environments with an EEG-based BCI through motor imagery. Biomed. Technik. 52, 86–91 (2005)CrossRefGoogle Scholar
  9. 9.
    Asensio-Cubero, J., Gan, J.Q., Palaniappan, R.: Multiresolution analysis over graphs for a motor imagery based online BCI game. Comput. Biol. Med. 68(1), 21–26 (2016)CrossRefGoogle Scholar
  10. 10.
    Billinger, M., Brunner, C., Müller-Putz, G.R.: SCoT: a Python toolbox for EEG source connectivity. Front. Neuroinformatics 8, 22 (2014)CrossRefGoogle Scholar
  11. 11.
    Sonkin, K.M., Stankevich, L.A., Khomenko, J.G., Nagornova, Z.V., Shemyakina, N.V.: Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand. Artif. Intell. Med. 63(2), 107–117 (2015)CrossRefGoogle Scholar
  12. 12.
    Stankevich, L.A., Sonkin, K.M., Shemyakina, N.V., Nagornova, Z.V., Khomenko, J.G., Perts, D.S., Koval, A.V.: Pattern decoding of rhythmic individual finger imaginary movements of one hand. Hum. Phisiology 42(1), 32–42 (2016)CrossRefGoogle Scholar
  13. 13.
    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, 668–677 (2005)CrossRefGoogle Scholar
  14. 14.
    Lotte, F., Congedo, M., Lecuyer, A., et al.: Review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), 1 (2007)CrossRefGoogle Scholar
  15. 15.
    Cortes, C., Vapnik, V.N.: Support-vector networks. Mach. Learn. 20(3), 273 (1995)zbMATHGoogle Scholar
  16. 16.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York (2004). http://www.kernel-methods.net CrossRefzbMATHGoogle Scholar
  17. 17.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(27), 1–27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Peter the Great Saint Petersburg Polytechnic UniversitySt. PetersburgRussia

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