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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
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
Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces, a review. Sensors 12, 1211–1279 (2012). https://doi.org/10.3390/s120201211
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
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
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
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors. 37, 32–64 (1995). https://doi.org/10.1518/001872095779049543
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
IBM Corp.: IBM SPSS Statistics for Windows, Version 24.0 (2016)
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
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
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
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
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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)