Evaluation of EEG Data for Zonal Affiliation of Brain Waves by Leads in a Robot Control Task | SpringerLink
Skip to main content
Springer Nature Link
Log in

Evaluation of EEG Data for Zonal Affiliation of Brain Waves by Leads in a Robot Control Task

  • Conference paper
  • First Online:
Interactive Collaborative Robotics (ICR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14214))

Included in the following conference series:

  • 488 Accesses

Abstract

The task of creating a neural interface for controlling a robotic system by means of an oculographic interface and bioelectric signals, is considered. The article highlights the results of scientific experimental research aimed at the evaluation of the representativeness of bioelectrical signals obtained by electroencephalography (EEG). The basic hypothesis is formulated and tested with the help of artificial neural network technology. The authors consider an experiment on the formation of steady-state visually evoked potentials in a group of people with the subsequent creation of an applied database. They describe an original approach for extracting representative features from the EEG signal. With the help of deep machine learning technology the representativeness of the data under study is evaluated. The main conclusions are formulated and the hypothesis that each brain lead reproduces unique waves which are characteristic of each brain zone is confirmed. The proposed model of a symmetric multilayer multi-adaptive direct propagation neuron can find its application in solving problems related to the processing of EEG signals. Based on the results of this study, the authors suggest that data on the bioelectrical activity of the brain can be uniquely identified, and thus used as control signals for various robotic devices.

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 7549
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 9437
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. Yang, D., Nguyen, T.H., Chung, W.Y.: A bipolar-channel hybrid brain-computer interface system for home automation control utilizing steady-state visually evoked potential and eye-blink signals. Sensors (Basel) 20(19), 5474 (2020). https://doi.org/10.3390/s20195474.

    Article  Google Scholar 

  2. Lin, J.-S., Yang, W.-C.: Wireless brain-computer interface for electric wheelchairs with EEG and eye-blinking signals. Int. J. Innovative Comput., Inf. Control 8, 6011–6024 (2012)

    Google Scholar 

  3. Rihana, S., Damien, P., Moujaess, T.: EEG-Eye blink detection system for brain computer interface. In: Pons, J.L., Torricelli, D., Pajaro, M. (eds.) Converging Clinical and Engineering Research on Neurorehabilitation, pp. 603–608. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-34546-3_98

    Chapter  Google Scholar 

  4. Musk, E.: An integrated brain-machine interface platform with thousands of channels. BioRxiv preprint, https://www.biorxiv.org/content/10.1101/703801v4. Last accessed 31 May 2023. https://doi.org/10.1101/703801

  5. Turovsky, Y., Wolf, D., Meshcheryakov, R., Iskhakova, A.: Dynamics of frequency characteristics of visually evoked potentials of electroencephalography during the work with brain-computer interfaces. In: Mahadeva Prasanna, S.R., Alexey Karpov, K., Samudravijaya, S.S., Agrawal, (eds.) Speech and Computer: 24th International Conference, SPECOM 2022, Gurugram, India, November 14–16, 2022, Proceedings, pp. 676–687. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-20980-2_57

    Chapter  Google Scholar 

  6. Tao, T., Yi, X., Xiaorong, G., Shangkai, G.: Chirp-modulated visual evoked potential as a generalization of steady state visual evoked potential. J. Neural Eng. 9(1), 016008 (2011). https://doi.org/10.1088/1741-2560/9/1/016008

    Article  Google Scholar 

  7. Kwak, N.-S., Müller, K.-R., Lee, S.-W.: Toward exoskeleton control based on steady state visual evoked potentials. In: 2014 International Winter Workshop on Brain-Computer Interface (BCI 2014), pp. 1–2. Gangwon, Korea (2014). https://doi.org/10.1109/iww-BCI.2014.6782571

  8. Balnytė, R., Uloziene, I., Rastenytė, D., Vaitkus, A., Malcienė, L., Laučkaitė, K.: Diagnostic value of conventional visual evoked potentials applied to patients with multiple sclerosis. Medicina 47(5), 263–269 (2011)

    Article  Google Scholar 

  9. Markand, Omkar N.: Visual evoked potentials. In: Clinical Evoked Potentials, pp. 83–137. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36955-2_3

    Chapter  Google Scholar 

  10. Chaudhary, U., Birbaumer, N., Curado, M.R.: Brain-machine interface (BMI) in paralysis. Ann. Phys. Rehabil. Med. 58(1), 9–13 (2015). https://doi.org/10.1016/j.rehab.2014.11.002

    Article  Google Scholar 

  11. Aminoff, M., Goodin, D.: Visual evoked potentials. J. Clin. Neurophysiol.: Official Publ. Am. Electroencephalographic Soc. 11, 493–499 (1994). https://doi.org/10.1097/00004691-199409000-00004

    Article  Google Scholar 

  12. Taylor, M., McCulloch, D.: Visual evoked potentials in infants and children. J. Clin. Neurophysiol.: Official Publ. American Electroencephalographic Soc. 9, 357–372 (1992). https://doi.org/10.1097/00004691-199207010-00004

    Article  Google Scholar 

  13. Liasis, A.: Visual evoked potentials. Acta Ophthalmol. 94 (2016). https://doi.org/10.1111/j.1755-3768.2016.0215

  14. Carter, J.: Visual evoked potentials. Clinical Neurophysiology, 311–322 (2011). https://doi.org/10.1093/med/9780195385113.003.0022

  15. Kwak, N.-S., Müller, 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 

  16. Wolf, D.A., Turovsky, Y.A., Meshcheryakov, R.V., Iskhakov, A.Y., Iskhakova, A.O.: EEG signal auto encoder, computer software, https://www1.fips.ru/iiss/document.xhtml?faces-redirect=true&id=d4eb144baee4f995556af206cde9da36. Last accessed 31 May 2023. (In Russ.)

  17. Naftali, T., Pereira, F.C., Bialek, W.: The information bottleneck method. In: Proceedings of the 37th Allerton Conference on Communication, Control and Computation, https://www.researchgate.net/publication/2844514_The_Information_Bottleneck_Method. Last accessed 31 May 2023

  18. Nguyen, H., Bottone, S., Kim, K., Chiang, M., Poor, H.V.: Adversarial Neural Networks for Error Correcting Codes (preprint), https://www.researchgate.net/publication/357267696_Adversarial_Neural_Networks_for_Error_Correcting_Codes. Last accessed 31 May 2023

  19. Kose, U., Deperlioglu, O., Alzubi, J., Patrut, B.: Diagnosing parkinson by using deep autoencoder neural network. In: Deep Learning for Medical Decision Support Systems. SCI, vol. 909, pp. 73–93. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-6325-6_5

    Chapter  Google Scholar 

  20. Mirjalili, V., Raschka, S., Namboodiri, A., Ross, A.: Semi-adversarial networks: convolutional autoencoders for imparting privacy to face images. In: 2018 International Conference on Biometrics (ICB), pp. 82–89. IEEE, Gold Coast, QLD, Australia (2018). https://doi.org/10.1109/ICB2018.2018.00023

  21. Meshcheryakov, R.V., Wolf, D.A., Turovsky, Y.A.: An autocoder of the electrical activity of the human brain. Bulletin of the South Ural State University, Series Mathematics. Mechanics. Physics 15(1), 34–42 (2023). https://doi.org/10.14529/mmph230104. (In Russ.)

  22. Bicego, M., Escolano, F.: On learning random forests for random forest-clustering. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3451–3458. IEEE, Milan, Italy (2021). https://doi.org/10.1109/ICPR48806.2021.9412014

  23. Olson, M.: Essays on Random Forest Ensembles, https://repository.upenn.edu/ dissertations/AAI10786136/. Last accessed 31 May 2023

  24. Nayyar, A., Mahapatra, B.: Effective classification and handling of incoming data packets in mobile Ad Hoc networks (MANETs) using random forest ensemble technique (RF/ET). In: Sharma, N., Chakrabarti, A., Balas, V.E. (eds.) Data Management, Analytics and Innovation. AISC, vol. 1016, pp. 431–444. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9364-8_31

    Chapter  Google Scholar 

  25. Fahim, A.: K and starting means for k-means algorithm. J. Comput. Sci. 55, 101445 (2021). https://doi.org/10.1016/j.jocs.2021.101445

    Article  Google Scholar 

Download references

Acknowledgements

The study was financially supported by the Russian Science Foundation under scientific project No. 23-19-00664.

Author information

Authors and Affiliations

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

    Daniyar Wolf, Yaroslav Turovsky, Anastasia Iskhakova & Roman Meshcheryakov

Authors
  1. Daniyar Wolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaroslav Turovsky
    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. Roman Meshcheryakov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anastasia Iskhakova .

Editor information

Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Andrey Ronzhin

  2. Institute of Control Systems of Ministry of Science and Education of the Republic of Azerbaijan, Baku, Azerbaijan

    Aminagha Sadigov

  3. V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, Moscow, Russia

    Roman Meshcheryakov

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wolf, D., Turovsky, Y., Iskhakova, A., Meshcheryakov, R. (2023). Evaluation of EEG Data for Zonal Affiliation of Brain Waves by Leads in a Robot Control Task. In: Ronzhin, A., Sadigov, A., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2023. Lecture Notes in Computer Science(), vol 14214. Springer, Cham. https://doi.org/10.1007/978-3-031-43111-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43111-1_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43110-4

  • Online ISBN: 978-3-031-43111-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation