Skip to main content
SpringerLink
Log in

Controlling Decisions by Head Electrical Signals

  • Conference paper
  • First Online:
Advances in System-Integrated Intelligence (SYSINT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 546))

Included in the following conference series:

  • 1155 Accesses

Abstract

This work considers the problem of using the electroencephalogram in a real context to control devices. The proposed work takes the data from the central and parietal brain areas to perform a steady state visually evoked potentials (SSVEP)-based Brain Computer Interface (BCI) model. The BCI output was retrieved by the human head electrical activity within a scenario that requires participants to think about a predefined image. The simulations were performed by 7 healthy participants 5 men and 2 women between 23 and 56 years old. A system composed by a Neural Network has been applied to develop the predictive model. The model developed can predict the human thinking with an accuracy more than 70% in the validation set for each participant.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • 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

References

  1. Nahavandi, S.: Industry 5.0-a human-centric solution. Sustainability 11(16) (2019). https://www.mdpi.com/2071-1050/11/16/4371

  2. Skobelev, P., Borovik, S.Y.: On the way from industry 4.0 to industry 5.0: from digital manufacturing to digital society. Industry 4.0 2(6), 307–311 (2017)

    Google Scholar 

  3. Bakay, R.: Brain-computer interfacing prospects and technical aspects. In: Schmidek and Sweet Operative Neurosurgical Techniques, pp. 1361–1372. Elsevier (2012)

    Google Scholar 

  4. Blum, T., Stauder, R., Euler, E., Navab, N.: Superman-like x-ray vision: towards brain-computer interfaces for medical augmented reality. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), vol. 2012, pp. 271–272. IEEE (2012)

    Google Scholar 

  5. Young, B.M., Williams, J., Prabhakaran, V.: BCI-FES: could a new rehabilitation device hold fresh promise for stroke patients? Exp. Rev. Med. Devices 11(6), 537–539 (2014)

    Article  Google Scholar 

  6. Bamdad, M., Zarshenas, H., Auais, M.A.: Application of BCI systems in neurorehabilitation: a scoping review. Disab. Rehabil. Assist. Technol. 10(5), 355–364 (2015)

    Article  Google Scholar 

  7. Blankertz, B., et al.: The berlin brain-computer interface: non-medical uses of BCI technology. Front. Neurosci. 198 (2010)

    Google Scholar 

  8. Nijholt, A.: BCI for games: a ‘State of the Art’ survey. In: Stevens, S.M., Saldamarco, S.J. (eds.) ICEC 2008. LNCS, vol. 5309, pp. 225–228. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89222-9_29

  9. Bi, L., Fan, X.-A., Liu, Y.: EEG-based brain-controlled mobile robots: a survey. IEEE Trans. Human Mach. Syst. 43(2), 161–176 (2013)

    Article  Google Scholar 

  10. Jahan, M., Khan, Y.U., Sharma, B.B.: Classification of EEG signals based on imaginary movement of right and left hand wrist. In: 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), pp. 193–196 (2014)

    Google Scholar 

  11. Hashimoto, Y., Ushiba, J.: EEG-based classification of imaginary left and right foot movements using beta rebound. Clin. Neurophysiol. 124(11), 2153–2160 (2013)

    Article  Google Scholar 

  12. Sakhavi, S., Guan, C., Yan, S.: Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5619–5629 (2018)

    Article  MathSciNet  Google Scholar 

  13. Zero, E., Bersani, C., Sacile, R.: EEG based BCI system for driver’s arm movements identification. In: Proceedings of the Automation, Robotics & Communications for Industry 4.0, vol. 77 (2021)

    Google Scholar 

  14. Umamaheswari, J., Akila, A.: An enhanced human speech emotion recognition using hybrid of PRNN and KNN. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 177–183 (2019)

    Google Scholar 

  15. Zero, E., Bersani, C., Zero, L., Sacile, R.: Towards real-time monitoring of fear in driving sessions. IFAC-PapersOnLine 52(19), 299–304 (2019)

    Article  Google Scholar 

  16. Binias, B., Myszor, D., Cyran, K.A.: A machine learning approach to the detection of pilot’s reaction to unexpected events based on EEG signals. In: Computational Intelligence and Neuroscience, vol. 2018 (2018)

    Google Scholar 

  17. Yin, E., Zhou, Z., Jiang, J., Yu, Y., Hu, D.: A dynamically optimized SSVEP brain-computer interface (BCI) speller. IEEE Trans. Biomed. Eng. 62(6), 1447–1456 (2014)

    Article  Google Scholar 

  18. Cao, L., Li, J., Ji, H., Jiang, C.: A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control. J. Neurosci. Methods 229, 33–43 (2014)

    Article  Google Scholar 

  19. Zheng, B.C., Li, Q., Xiang, Y.X., Hernandez, J., Mann, S.: Brain and visual controlled mobility scooter

    Google Scholar 

  20. Razoumnikova, O.M.: Functional organization of different brain areas during convergent and divergent thinking: an EEG investigation. Cognit. Brain Res. 10(1–2), 11–18 (2000)

    Article  Google Scholar 

  21. Singh, V., Veer, K., Sharma, R., Kumar, S.: Comparative study of fir and IIR filters for the removal of 50 Hz noise from EEG signal. Int. J. Biomed. Eng. Technol. 22(3), 250–257 (2016)

    Article  Google Scholar 

  22. Zero, E., Bersani, C., Sacile, R.: Identification of brain electrical activity related to head yaw rotations. Sensors 21(10), 3345 (2021)

    Article  Google Scholar 

  23. Dongare, A., Kharde, R., Kachare, A.D., et al.: Introduction to artificial neural network. Int. J. Eng. Innovat. Technol. 2(1), 189–194 (2012)

    Google Scholar 

  24. Lv, C., et al.: Levenberg-marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system. IEEE Trans. Indust. Inform. 14(8), 3436–3446 (2017)

    Google Scholar 

  25. Feng, D., Chen, L., Chen, P.: Intention recognition of upper limb movement on electroencephalogram signal based on CSP-CNN. In: 2021 5th International Conference on Robotics and Automation Sciences (ICRAS), pp. 267–271. IEEE (2021)

    Google Scholar 

  26. Cohen, L.H.: Life Events and Psychological Functioning: Theoretical and Methodological Issues, vol. 90. SAGE Publications, Incorporated (1988)

    Google Scholar 

Download references

Acknowledgment

This study is supported by the SysE2021 project (2021–2023), “Centre d’excellence transfrontalier pour la formation en ingénierie de systèmes" developed in the framework of the Interreg V-A France-Italie (ALCOTRA) (2014–2020), Programme de coopération transfrontalière européenne entre la France et l’Italie.

Author information

Authors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy

    Enrico Zero, Alessandro Bozzi, Simone Graffione & Roberto Sacile

Authors
  1. Enrico Zero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessandro Bozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simone Graffione
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roberto Sacile
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enrico Zero .

Editor information

Editors and Affiliations

  1. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Maurizio Valle

  2. Fraunhofer Institute for Manufacturing Technology and Advanced Materials, Bremen, Germany

    Dirk Lehmhus

  3. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Christian Gianoglio

  4. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Edoardo Ragusa

  5. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Lucia Seminara

  6. Computer Science and Mathematics, University of Bremen, Bremen, Germany

    Stefan Bosse

  7. Department of Electrical and Electronics Engineering (EEE), Lebanese International University (LIU), Beirut, Lebanon

    Ali Ibrahim

  8. BIBA, University of Bremen, Bremen, Germany

    Klaus-Dieter Thoben

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

Zero, E., Bozzi, A., Graffione, S., Sacile, R. (2023). Controlling Decisions by Head Electrical Signals. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16281-7_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16280-0

  • Online ISBN: 978-3-031-16281-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation