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)


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.


Brain-computer interface Neural networks Classifying 



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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