Skip to main content

Evaluation of Visual Parameters to Control a Visual ERP-BCI Under Single-Trial Classification

  • Conference paper
  • First Online:
Advances in Computational Intelligence (IWANN 2023)

Abstract

A brain-computer interface (BCIs) based on event-related potentials (ERPs) is a technology that provides a communication channel between a device and a user through their brain activity. These systems could be used to assist and facilitate decision making in applications such as an air traffic controller (ATC). Thus, this work attempts to be an approximation to determine whether it is possible to detect the stimulus through a single presentation of a stimulus (single-trial classification) and furthermore, to evaluate the effects of the type of stimulus to be detected, or not knowing the position of the stimulus appearance in an ERP-BCI. This experiment has involved six participants in four experimental conditions. Two conditions varied only in the type of stimulus used, faces (a type of stimulus that has shown high performance in previous ERP-BCI proposals) versus radar planes; and two conditions varied in the prior knowledge of where the stimulus would appear on the screen (knowing vs. not knowing). The results suggest that the use of single-trial classification could be adequate to correctly detect the desired stimulus using and ERP-BCI. In addition, the results reveal no significant effect on either of the two factors. Therefore, it seems that radar planes may be as suitable stimuli as faces and that not knowing the location of the target stimulus is not a significant problem, at least in a standard BCI scenario without distracting stimuli. Therefore, future studies should consider these findings for the design of an ATC using an ERP-BCI for stimulus detection.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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

Similar content being viewed by others

References

  1. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002). https://doi.org/10.1016/S1388-2457(02)00057-3

    Article  PubMed  Google Scholar 

  2. Xu, L., Xu, M., Jung, T.P., Ming, D.: Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface (2021). https://doi.org/10.1007/s11571-021-09676-z

  3. Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces, a review. Sensors 12, 1211–1279 (2012). https://doi.org/10.3390/s120201211

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bonci, A., Fiori, S., Higashi, H., Tanaka, T., Verdini, F.: An introductory tutorial on brain–computer interfaces and their applications. Electron 10, 1–43 (2021). https://doi.org/10.3390/electronics10050560

    Article  Google Scholar 

  5. Gaume, A., Dreyfus, G., Vialatte, F.B.: A cognitive brain–computer interface monitoring sustained attentional variations during a continuous task. Cogn. Neurodyn. 13, 257–269 (2019). https://doi.org/10.1007/s11571-019-09521-4

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bhattacharyya, S., Valeriani, D., Cinel, C., Citi, L., Poli, R.: Anytime collaborative brain–computer interfaces for enhancing perceptual group decision-making. Sci. Rep. 11, 1–16 (2021). https://doi.org/10.1038/s41598-021-96434-0

    Article  CAS  Google Scholar 

  7. Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors. 37, 32–64 (1995). https://doi.org/10.1518/001872095779049543

    Article  Google Scholar 

  8. Aricò, P., et al.: Adaptive automation triggered by EEG-based mental workload index: a passive brain-computer interface application in realistic air traffic control environment. Front. Hum. Neurosci. 10, 1–13 (2016). https://doi.org/10.3389/fnhum.2016.00539

    Article  Google Scholar 

  9. Di Flumeri, G., et al.: Brain–computer interface-based adaptive automation to prevent out-of-the-loop phenomenon in air traffic controllers dealing with highly automated systems. Front. Hum. Neurosci. 13 (2019). https://doi.org/10.3389/fnhum.2019.00296

  10. Aricò, P., Borghini, G., Di Flumeri, G., Colosimo, A., Pozzi, S., Babiloni, F.: A passive brain–computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks. Prog. Brain Res. 228, 295–328 (2016). https://doi.org/10.1016/bs.pbr.2016.04.021

    Article  PubMed  Google Scholar 

  11. Li, W., Li, R., Xie, X., Chang, Y.: Evaluating mental workload during multitasking in simulated flight. Brain Behav. 12, 1–11 (2022). https://doi.org/10.1002/brb3.2489

    Article  Google Scholar 

  12. Boyle, L.N., Tippin, J., Paul, A., Rizzo, M.: Driver performance in the moments surrounding a microsleep. Transp. Res. Part F Traffic Psychol. Behav. 11, 126–136 (2008). https://doi.org/10.1016/j.trf.2007.08.001

  13. Kaufmann, T., Schulz, S.M., Grünzinger, C., Kübler, A.: Flashing characters with famous faces improves ERP-based brain-computer interface performance. J. Neural Eng. 8, 056016 (2011). https://doi.org/10.1088/1741-2560/8/5/056016

    Article  CAS  PubMed  Google Scholar 

  14. Pfabigan, D.M., Sailer, U., Lamm, C.: Size does matter! Perceptual stimulus properties affect event-related potentials during feedback processing. Psychophysiology 52, 1238–1247 (2015). https://doi.org/10.1111/psyp.12458

    Article  PubMed  Google Scholar 

  15. Fernández-Rodríguez, Á., Darves-Bornoz, A., Velasco-Álvarez, F., Ron-Angevin, R.: Effect of Stimulus Size in a Visual ERP-Based BCI under RSVP. Sensors. 22, (2022). https://doi.org/10.3390/s22239505

  16. Li, Y., Bahn, S., Nam, C.S., Lee, J.: effects of luminosity contrast and stimulus duration on user performance and preference in a P300-based brain-computer interface. Int. J. Hum. Comput. Interact. 30, 151–163 (2014). https://doi.org/10.1080/10447318.2013.839903

    Article  CAS  Google Scholar 

  17. Fernández-Rodríguez, A., Velasco-Álvarez, F., Ron-Angevin, R.: Review of real brain-controlled wheelchairs. J. Neural Eng. 13 (2016). https://doi.org/10.1088/1741-2560/13/6/061001

  18. Alrumiah, S.S., Alhajjaj1, L.A., Alshobaili, J.F., Ibrahim, D.M.: A review on brain-computer interface spellers: P300 speller. Biomed. Commun. 13, 1191–1199 (2020). https://doi.org/10.1016/s0022-4804(03)00693-0

  19. Cecotti, H., Ries, A.J.: Best practice for single-trial detection of event-related potentials: application to brain-computer interfaces. Int. J. Psychophysiol. 111, 156–169 (2017). https://doi.org/10.1016/j.ijpsycho.2016.07.500

    Article  PubMed  Google Scholar 

  20. Tian, Y., Zhang, H., Pang, Y., Lin, J.: Classification for single-trial N170 during responding to facial picture with emotion. Front. Comput. Neurosci. 12 (2018). https://doi.org/10.3389/fncom.2018.00068

  21. Goljahani, A., D’Avanzo, C., Silvoni, S., Tonin, P., Piccione, F., Sparacino, G.: Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface. Comput. Math. Methods Med. 2014 (2014). https://doi.org/10.1155/2014/731046

  22. Zhang, X., Jin, J., Li, S., Wang, X., Cichocki, A.: Evaluation of color modulation in visual P300-speller using new stimulus patterns. Cogn. Neurodyn. 0123456789, (2021). https://doi.org/10.1007/s11571-021-09669-y

  23. Pires, G., Nunes, U., Castelo-Branco, M.: Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients. Clin. Neurophysiol. 123, 1168–1181 (2012). https://doi.org/10.1016/j.clinph.2011.10.040

  24. Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system (2004). https://doi.org/10.1109/TBME.2004.827072

  25. IBM Corp.: IBM SPSS Statistics for Windows, Version 24.0 (2016)

    Google Scholar 

  26. Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., Volosyak, I.: Brain–computer interface spellers: a review. Brain Sci. 8 (2018). https://doi.org/10.3390/brainsci8040057

  27. Kübler, A., et al.: The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications. PLoS ONE 9, 1–22 (2014). https://doi.org/10.1371/journal.pone.0112392

    Article  CAS  Google Scholar 

  28. Lotte, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15 (2018). https://doi.org/10.1088/1741-2552/aab2f2

  29. Kellicut-Jones, M.R., Sellers, E.W.: P300 brain-computer interface: comparing faces to size matched non-face stimuli. Brain-Comput. Interfaces 5, 30–39 (2018). https://doi.org/10.1080/2326263X.2018.1433776

    Article  Google Scholar 

  30. Ron-Angevin, R., et al.: Performance analysis with different types of visual stimuli in a BCI-Based speller under an RSVP paradigm. Front. Comput. Neurosci. 14 (2021). https://doi.org/10.3389/fncom.2020.587702

Download references

Acknowledgements

This work was partially supported by the project PID2021-127261OB-I00 (SICODIS), funded by MCIN (Ministerio de Ciencia e Innovación) /AEI (Agencia Estatal de Investigación) /https://doi.org/10.13039/501100011033/ FEDER, UE (Fondo Europeo de Desarrollo Regional). The work was also partially supported by the University of Málaga (Universidad de Málaga) and by THALES AVS in the context of a GIS Albatros project. The authors would also like to thank all participants for their cooperation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ricardo Ron-Angevin .

Editor information

Editors and Affiliations

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

Fernández-Rodríguez, Á., Ron-Angevin, R., Velasco-Álvarez, F., Diaz-Pineda, J., Letouzé, T., André, JM. (2023). Evaluation of Visual Parameters to Control a Visual ERP-BCI Under Single-Trial Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43078-7_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43077-0

  • Online ISBN: 978-3-031-43078-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics