Neuro-Computer Interface Control of Cyber-Physical Systems | SpringerLink
Skip to main content
Springer Nature Link
Log in

Neuro-Computer Interface Control of Cyber-Physical Systems

  • Conference paper
  • First Online:
High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1526))

  • 452 Accesses

Abstract

The paper proposes an approach to and solves the problem of controlling a robot by using neural interface technology, describes the general scheme and working principle of the main idea of non-invasive neural interface control of a robot using the original convolutional neural network. The authors describe the principles of an original convolutional neural network and an approach to the modern network design, present a model of a one-dimensional convolutional network based on the principles of a human inner ear. The structure of a software package is proposed. The results of a comparison of algorithms for the analysis of human brain evoked potentials used in the design of brain-computer interfaces are presented. The authors used the Fourier transform algorithm and the multidimensional synchronization index (MSI) algorithm in various modifications to perform the experiment. Analysis of the initial signal, the accumulated evoked potential, in addition to the accumulated evoked potential spectrum were proposed as variations. Linear correlation was also evaluated with analysis using a user-derived reference signal sample and various variations of wavelet filtering. In addition, model signals, which were a combination of white noise and a harmonic oscillation simulating a stable visual evoked potential, were used. The best results (error rate <10%) with an analysis time of 3 s were obtained for the MSI of the original signal, MSI with the Fourier transform. Also in this list, there is a MSI where the wavelet filtering result of coherent accumulation was used as an etalon, a linear correlation coefficient. In a MSI the evoked potential, recovered after the wavelet transform, was used as an etalon.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 10295
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 12869
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lebedev, M.A., Nicolelis, M.A.: Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation. Physiol. Rev. 97(2), 767–837 (2017). https://doi.org/10.1152/physrev.00027.2016

    Article  Google Scholar 

  2. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin Neurophysiol. 113(6), 767–791 (2002). https://doi.org/10.1016/s1388-2457(02)00057-3

    Article  Google Scholar 

  3. Galin, R.R., Meshcheryakov, R.V.: Human-robot interaction efficiency and human-robot collaboration. In: Kravets, A.G. (ed.) Robotics: Industry 4.0 Issues & New Intelligent Control Paradigms. SSDC, vol. 272, pp. 55–63. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37841-7_5

    Chapter  Google Scholar 

  4. Galin, R., Meshcheryakov, R.: Review on human–robot interaction during collaboration in a shared workspace. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2019. LNCS (LNAI), vol. 11659, pp. 63–74. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26118-4_7

    Chapter  Google Scholar 

  5. Meshcheryakov, R.V., Iskhakov, A.Y., Evsutin, O.O.: Analysis of modern methods to ensure data integrity in cyber-physical system management protocols. Inf. Autom. 19(5), 1089–1122 (2020). https://doi.org/10.15622/ia.2020.19.5.7

    Article  Google Scholar 

  6. Kharchenko, S., Meshcheryakov, R., Turovsky, Y., Volf, D.: Implementation of robot–human control bio-interface when highlighting visual-evoked potentials based on multivariate synchronization index. In: Ronzhin, A., Shishlakov, V. (eds.) Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings.” SIST, vol. 187, pp. 225–236. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5580-0_18

    Chapter  Google Scholar 

  7. Kharchenko, S., Turovsky, Y., Meshcheryakov, R., Iskhakova, A.: Restrictions of the measurement system and a patient when using visually evoked potentials. In: Proceedings of the 12th International Conference on Developments in eSystems Engineering (DeSE), pp. 15–19. IEEE, Kazan (2019). https://doi.org/10.1109/DeSE.2019.00013

  8. Fatih, D.A.: Bio-inspired filter banks for SSVEP-based brain-computer interfaces. In: 2016 IEEE International Conference on Biomedical and Health Informatics (BHI), pp. 144–147. IEEE, Las Vegas (2016). https://doi.org/10.1109/BHI.2016.7455855

  9. Zhu, D., Bieger, J., Molina, G., Aarts, R.M.: A Survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. 2010, 1–12 (2010). https://doi.org/10.1155/2010/702357

    Article  Google Scholar 

  10. Farwell, L.A., Donchin, E.: Talking off the top of your head: towards mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988). https://doi.org/10.1016/0013-4694(88)90149-6

    Article  Google Scholar 

  11. Kwak, N.-S., Muller, K.-R., Lee, S.-W.: A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLoS ONE 12(2), 1–20 (2017). https://doi.org/10.1371/journal.pone.0172578

    Article  Google Scholar 

  12. Middendorf, M., McMillan, G., Calhoun, G., Jones, K.: Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans. Rehabil. Eng. 8(2), 211–214 (2000). https://doi.org/10.1109/86.847819

    Article  Google Scholar 

  13. Haq S., Jackson, P.J.: Multimodal emotion recognition. In: Machine Audition: Principles, Algorithms and Systems, pp. 398–423. IGI Global (2011). https://doi.org/10.4018/978-1-61520-919-4.ch017

  14. Han, K., Yu, D., Tashev, I.: Speech emotion recognition using deep neural network and extreme learning machine. In: INTERSPEECH, Singapore, Malaysia, pp. 223–227 (2014)

    Google Scholar 

  15. Minsky, M.: The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon and Schuster, New York (2007)

    Google Scholar 

  16. Yan, J., Chen, S., Deng, S.: A EEG-based emotion recognition model with rhythm and time characteristics. Brain Inf. 6(1), 1–8 (2019). https://doi.org/10.1186/s40708-019-0100-y

    Article  Google Scholar 

  17. Yue, K., Wang, D.: EEG-based 3D visual fatigue evaluation using CNN. Electronics 8(11), 1208 (2019). https://doi.org/10.3390/electronics8111208

    Article  Google Scholar 

  18. Wang, Y., Wang, Y., Cheng, C., Jung, T.: Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, pp. 5271–5274 (2013). https://doi.org/10.1109/EMBC.2013.6610738

  19. Volosyak, I.: SSVEP based Bremen-BCI boosting information transfer rates. J. Neural Eng. 8(3), 036020 (2011). https://doi.org/10.1088/1741-2560/8/3/036020

    Article  Google Scholar 

  20. Resalat, S.N., Setarehdan, S.K.: An Improved SSVEP based BCI system using frequency domain feature classification. Am. J. Biomed. Eng. 3(1), 1–8 (2013). https://doi.org/10.5923/j.ajbe.20130301.01

    Article  Google Scholar 

  21. Iskhakova, A., Alekhin, M., Bogomolov, A.: Time-frequency transforms in analysis of non-stationary quasi-periodic biomedical signal patterns for acoustic anomaly detection. Inf. Control Syst. 1, 15–23 (2020). https://doi.org/10.31799/1684-8853-2020-1-15-23

    Article  Google Scholar 

  22. Zhang, Y., Peng, X., Cheng, K., Yao, D.: Multivariate synchronization index for frequency recognition of SSVEP-based brain–computer interface. J. Neurosci. Methods 221, 32–40 (2014). https://doi.org/10.1016/j.jneumeth.2013.07.018

    Article  Google Scholar 

  23. Belobrodsky, V.A., Kurgalin, S.D., Turovsky, Y., Vahtin, A.A.: Developing a genetic algorithm for digital filters design to classify biomedical signals and testing the algorithm on known property signals. Biomed. Radioelectron. 2, 56–64 (2015). (In Russ.)

    Google Scholar 

  24. Turovsky, Y., Kurgalin, S.D., Vahtin, A.A., Borzunov, S.V., Belobrodsky, V.A.: Event-related brain potential investigation using the adaptive wavelet recovery method. Biophysics 60(3), 443–448 (2015). https://doi.org/10.1134/S0006350915030203

    Article  Google Scholar 

  25. Turovsky, Y.A., Borzunov, S.V., Danilova, A.V., Glagoleva, E.P.: Dynamics of involuntary formation of EEG correlation patterns by biofeedback mechanism. Ulyanovsk Med. Biol. J. 2, 90–99 (2020). https://doi.org/10.34014/2227-1848-2020-2-90-99

    Article  Google Scholar 

  26. Turovsky, Y.A.: Comparative characteristics of the algorithms of detection steady state visually evoked potentials of the brain on an electroencephalogram. Cifrovaya Obrabotka Signalov 1, 51–55 (2018)

    Google Scholar 

Download references

Acknowledgements

The reported study was partially funded by RFBR, project number 19-29-01156.

Author information

Authors and Affiliations

  1. V. A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, Profsoyuznaya Street 65, 117997, Moscow, Russia

    Yaroslav Turovskiy, Daniyar Volf, Anastasia Iskhakova & Andrey Iskhakov

Authors
  1. Yaroslav Turovskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniyar Volf
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasia Iskhakova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrey Iskhakov
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Altai State University, Barnaul, Russia

    Vladimir Jordan

  2. MIREA - Russian Technological University, Moscow, Russia

    Ilya Tarasov

  3. Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia

    Vladimir Faerman

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Turovskiy, Y., Volf, D., Iskhakova, A., Iskhakov, A. (2022). Neuro-Computer Interface Control of Cyber-Physical Systems. In: Jordan, V., Tarasov, I., Faerman, V. (eds) High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production. HPCST 2021. Communications in Computer and Information Science, vol 1526. Springer, Cham. https://doi.org/10.1007/978-3-030-94141-3_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94141-3_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94140-6

  • Online ISBN: 978-3-030-94141-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation