Abstract
Brain–computer interfaces (BCIs) are a form of technology that read a user’s neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as “BCI illiteracy,” and a body of research has emerged aiming to characterize, predict, and solve this perceived problem. However, BCI illiteracy is an inadequate concept for explaining difficulty that users face in operating BCI systems. BCI illiteracy is a methodologically weak concept; furthermore, it relies on the flawed assumption that BCI users possess physiological or functional traits that prevent proficient performance during BCI use. Alternative concepts to BCI illiteracy may offer better outcomes for prospective users and may avoid the conceptual pitfalls that BCI illiteracy brings to the BCI research process.
Similar content being viewed by others
Notes
At times this critique will reference similar normative phrasing; the use of these terms in this paper are meant relative to and entangled with existing norms in BCI research rather than as independent, self-sufficient categories.
A more detailed discussion of the similarities or differences between BCI skill and literacy, and the accuracy of such terminology, is provided by Brendan Allison and Christa Neuper (2010).
This paper uses the phrase BCI illiteracy, as it is by far the most popular of these terms, but the arguments presented will apply to any concept similar to BCI illiteracy regardless of the specific terminology used.
Individual BCI researchers may not go through all of these steps—some just stop at labeling, some only propose to design BCIs for illiterate populations, etc.
References
Ahn, M., Cho, H., Ahn, S., & Jun, S. C. (2013). High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PLoS ONE, 8(11), e80886.
Ahn, M., & Jun, S. C. (2015). Performance variation in motor imagery brain–computer interface: A brief review. Journal of Neuroscience Methods, 243, 103–110.
Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A. (2010). BCI demographics: How many (and what kinds of) people can use an SSVEP BCI? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(2), 107–116.
Allison, B. Z., & Neuper, C. (2010). Could anyone use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain–computer interfaces (pp. 35–54). London, UK: Springer.
Allison, B. Z., Wolpaw, E. W., & Wolpaw, J. R. (2007). Brain–computer interface systems: progress and prospects. Expert Review of Medical Devices, 4(4), 463–474.
Banville, H., & Falk, T. H. (2016). Recent advances and open challenges in hybrid brain–computer interfacing: a technological review of non-invasive human research. Brain–Computer Interfaces, 3(1), 9–46.
Blankertz, B., Sannelli, C., Halder, S., Hammer, E. M., Kübler, A., Müller, K. R., Curio, G., & Dickhaus, T. (2010). Neurophysiological predictor of SMR-based BCI performance. Neuroimage, 51(4), 1303–1309.
Carabalona, R. (2017). The role of the interplay between stimulus type and timing in explaining BCI-illiteracy for visual P300-based brain–computer interfaces. Frontiers in Neuroscience, 11, 363.
Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017). Heading for new shores! Overcoming pitfalls in BCI design. Brain–Computer Interfaces, 4(1–2), 60–73.
Emma W. (2017). People: The strongest link. Resource document. National Cyber Security Centre. https://www.ncsc.gov.uk/information/people-strongest-link. Accessed 28 March 2017.
Friedrich, E. V. C., Neuper, C., & Scherer, R. (2013). Whatever works: A systematic user-centered training protocol to optimize brain–computer interfacing individually. PLoS ONE, 8(9), e76214.
Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., & Pfurtscheller, G. (2003). How many people are able to operate an EEG-based brain–computer interface (BCI)? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 145–147.
Hacking, I. (1995). The looping effects of human kinds. In D. Sperber, D. Premack, & A. J. Premack (Eds.), Symposia of the Fyssen Foundation. Causal Cognition: A Multidisciplinary Debate (pp. 351–394). New York, NY: Clarendon Press/Oxford University Press.
Jeunet, C., Cellard, A., Subramanian, S., Hachet, M., N’Kaoua, B., & Lotte, F. (2014). How well can we learn with standard BCI training approaches? A pilot study, Presented at 6th International Brain–Computer Interface Conference, Graz, Austria, September 2014.
Kaufmann, T., Schulz, S. M., Köblitz, A., Renner, G., Wessig, C., & Kübler, A. (2013). Face stimuli effectively prevent brain–computer interface inefficiency in patients with neurodegenerative disease. Clinical Neurophysiology, 124(5), 893–900.
Kaufmann, T., Völker, S., Gunesch, L., & Kübler, A. (2012). Spelling is just a click away–a user-centered Brain–computer interface including auto-calibration and predictive text entry. Frontiers in Neuroscience, 6, 72.
Kübler, A., Holz, E. M., Riccio, A., Zickler, C., Kaufmann, T., Kleih, S. C., Staiger-Sälzer, P., Desideri, L., Hoogerwerf, E. J., & Mattia, D. (2014). The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications. PLoS ONE, 9(12), e112392.
Kübler, A., Mattia, D., Rupp, R., & Tangermann, M. (2013). Facing the challenge: Bringing brain–computer interfaces to end-users. Artificial Intelligence in Medicine, 59(2), 55–60.
Kübler, A., & Müller, K. (2007). An introduction to brain–computer interfacing. In G. Dornhege, J. R. Millán, T. Hinterberger, D. J. McFarland, & K. Müller (Eds.), Toward brain–computer interfacing (pp. 1–25). Cambridge, MA: The MIT Press.
Lotte, F., Larrue, F., & Mühl, C. (2013). Flaws in current human training protocols for spontaneous Brain–computer interfaces: Lessons learned from instructional design. Frontiers in Human Neuroscience, 7, 568.
McCane, L. M., Sellers, E. W., McFarland, D. J., Mak, J. N., Carmack, C. S., Zeitlin, D., Wolpaw, J. R., & Vaughan, T. M. (2014). Brain–computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 15(3–4), 207–215.
Myrden, A., & Chau, T. (2015). Effects of user mental state on EEG–BCI performance. Frontiers in Human Neuroscience, 9, 308.
Nijboer, F., Sellers, E. W., Mellinger, J., Jordan, M. A., Matuz, T., Furdea, A., Halder, S., Mochty, U., Krusienski, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008). A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology, 119(8), 1909–1916.
Pasqualotto, E., Federici, S., & Belardinelli, M. O. (2012). Toward functioning and usable brain–computer interfaces (BCIs): A literature review. Disability and Rehabilitation: Assistive Technology, 7(2), 89–103.
Pasqualotto, E., Matuz, T., Federici, S., Ruf, C. A., Bartl, M., Olivetti Belardinelli, M., Birbaumer, N., & Halder, S. (2015). Usability and workload of access technology for people with severe motor impairment: A comparison of Brain–computer interfacing and eye tracking. Neurorehabilitation and Neural Repair, 29(10), 950–957.
Peters, B., Mooney, A., Oken, B., & Fried-Oken, M. (2016). Soliciting BCI user experience feedback from people with severe speech and physical impairments. Brain–Computer Interfaces, 3(1), 47–58.
Pfurtscheller, G., Allison, B. Z., Bauernfeind, G., Brunner, C., Solis Escalante, T., Scherer, R., Zander, T. O., Mueller-Putz, G., Neuper, C., & Birbaumer, N. (2010). The hybrid BCI. Frontiers in Neuroscience, 4, 3.
Riemer-Reiss, M. L., & Wacker, R. R. (2000). Factors associated with assistive technology discontinuance among individuals with disabilities. Journal of Rehabilitation, 66(3), 44–50.
Schreuder, M., Riccio, A., Risetti, M., Dähne, S., Ramsay, A., Williamson, J., Mattia, D., & Tangermann, M. (2013). User-centered design in brain–computer interfaces—A case study. Artificial Intelligence in Medicine, 59(2), 71–80.
Sellers, E., Krusienski, D., McFarland, D., Vaughan, T., & Wolpaw, J. (2006). A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance. Biological Psychology, 73, 242–252.
Sexton, C. A. (2015). The overlooked potential for social factors to improve effectiveness of brain–computer interfaces. Frontiers in Systems Neuroscience, 9(233), 693–695.
Shu, X., Chen, S., Yao, L., Sheng, X., Zhang, D., Jiang, N., Jia, J., & Zhu, X. (2018). Fast recognition of BCI-inefficient users using physiological features from EEG Signals: A screening study of stroke patients. Frontiers in Neuroscience, 12, 93.
Silvers, A. (2009). An essay on modeling: The social model of disability. Philosophical reflections on disability (pp. 19–36). Netherlands: Springer.
Suk, H., Fazli, S., Mehnert, J., Müller, K., & Lee, S. (2014). Predicting BCI subject performance using probabilistic spatio-temporal filters. PLoS ONE, 9(2), e87056.
Thomas, E., Dyson, M., & Clerc, M. (2013). An analysis of performance evaluation for motor-imagery based BCI. Journal of Neural Engineering, 10, e031001.
Viduarre, C., & Blankertz, B. (2010). Towards a cure for BCI illiteracy. Brain Topography, 23(2), 194–198.
Whitbeck, Caroline. (1996). Ethics as design: Doing justice to moral problems. The Hastings Center Report, 26(3), 9–16.
Yao, L., Sheng, X., Zhang, D., Jiang, N., Farina, D., & Zhu, X. (2017). A BCI system based on somatosensory attentional orientation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(1), 81–90.
Acknowledgements
The author would like to thank the Neuroethics group at the Center for Sensorimotor Neural Engineering, especially Dr. Laura Specker Sullivan, Timothy Brown, Marion Boulicault and Dr. Sara Goering for their help in refining this critique from early to final drafts.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Thompson, M.C. Critiquing the Concept of BCI Illiteracy. Sci Eng Ethics 25, 1217–1233 (2019). https://doi.org/10.1007/s11948-018-0061-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11948-018-0061-1