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Cyber-Physical System Control Based on Brain-Computer Interface

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

Abstract

The study describes approaches of direct and supervisory control of cyber-physical systems based on a brain-computer interface. The interface is the main component of the control system, performing electroencephalographic signal decoding, which includes several steps: filtering, artefact detection, feature extraction, and classification. In this study, a classifier based on deep neural networks was developed and applied. Description of the classifiers based on convolutional neural network is given. The developed classifier demonstrated accuracy 73 ± 5% of decoding four classes of imaginary movements. Prospects of using non-invasive brain-computer interface for control of cyber-physical systems, in particular, mobile robots for maintenance of immobilized patients and devices for rehabilitation of post-stroke patients are discussed.

Keywords

Brain-computer interface Neural networks Classifying 

Notes

Acknowledgements

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

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Filipp Gundelakh
    • 1
  • Lev Stankevich
    • 2
  • Nikolay V. Kapralov
    • 1
    Email author
  • Jaroslav V. Ekimovskii
    • 1
  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySaint PetersburgRussia
  2. 2.St. Petersburg Institute of Informatics and Automatics of RASSaint PetersburgRussia

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