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Could Anyone Use a BCI?

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Brain-Computer Interfaces

Part of the book series: Human-Computer Interaction Series ((HCIS))

Abstract

Brain-computer interface (BCI) systems can provide communication and control for many users, but not all users. This problem exists across different BCI approaches; a “universal” BCI that works for everyone has never been developed. Instead, about 20% of subjects are not proficient with a typical BCI system. Some groups have called this phenomenon “BCI illiteracy”. Some possible solutions have been explored, such as improved signal processing, training, and new tasks or instructions. These approaches have not resulted in a BCI that works for all users, probably because a small minority of users cannot produce detectable patterns of brain activity necessary to a particular BCI approach. We also discuss an underappreciated solution: switching to a different BCI approach. While the term “BCI illiteracy” elicits interesting comparisons between BCIs and natural languages, many issues are unclear. For example, comparisons across different studies have been problematic since different groups use different performance thresholds, and do not account for key factors such as the number of trials or size of the BCI’s alphabet. We also discuss challenges inherent in establishing widely used terms, definitions, and measurement approaches to facilitate discussions and comparisons among different groups.

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References

  • Allison BZ (in press) Toward ubiquitous BCIs. In Graimann B, Allison BZ, Pfurtscheller G (eds) Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. Springer, Berlin

    Google Scholar 

  • Allison BZ, Pineda JA (2006) Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: Implications for a BCI system. Int J Psychophysiol 59:127–140

    Article  Google Scholar 

  • Allison BZ, Wolpaw EW, Wolpaw JR (2007) Brain computer interface systems: Progress and prospects. In: Poll E (ed) British Review of Medical Devices, Jul; 4(4):463–474

    Google Scholar 

  • Allison BZ, McFarland DJ, Schalk G, Zheng SD, Moore Jackson M, Wolpaw JR (2008) Towards an independent SSVEP brain computer interface. Clin Neurophysiol 119(2):399–408

    Article  Google Scholar 

  • Allison BZ, Valbuena D, Lueth T, Teymourian A, Volosyak I, Gräser A (2010a) BCI demographics: How many (and what kinds of) people can use an SSVEP BCI? IEEE Trans Neural Syst Rehabil Eng. DOI 10.1109/TNSRE.2009.2039495

    Google Scholar 

  • Allison BZ, Brunner C, Kaiser V, Müller-Putz G, Neuper C, Pfurtscheller G (2010b) A hybrid brain-computer interface based on imagined movement and visual attention. J Neural Eng 7(2):26007

    Article  Google Scholar 

  • Bin GY, Gao XR, Wang YJ, Hong B, Gao SK (2009) IEEE Comput Intell Mag 4(4):22 –26

    Article  Google Scholar 

  • Blakely T, Miller KJ, Zanos SP, Rao RP, Ojemann JG (2009) Robust long-term control of an electrocorticographic brain-computer interface with fixed parameters. Neurosurg Focus 27(1):E13

    Article  Google Scholar 

  • Blankertz B, Müller K-R, Curio G, Vaughan TM, Schalk G, Wolpaw JR, Schlögl A, Neuper C, Pfurtscheller G, Hinterberger T, Schröder M, Birbaumer N (2004) Progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng 51(6):1044–1051

    Article  Google Scholar 

  • Blankertz B, Losch Y, Krauledat M, Dornhege G, Curio G, Müller K-R (2008) The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects. IEEE Trans Biomed Eng 55:2452–2462

    Article  Google Scholar 

  • Brunner C, Allison BZ, Krusienski DJ, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G (2010) Improved signal processing approaches for a hybrid brain-computer interface simulation. J Neurosci Methods 188(1):165–173

    Article  Google Scholar 

  • Buttfield A, Ferrez PW, Millán JR (2006) Towards a robust BCI: Error potentials and online learning. IEEE Trans Neural Syst Rehabil Eng 14(2):164–168

    Article  Google Scholar 

  • Cheng M, Gao XR, Gao SG, Xu DF (2002) Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng 49(10):1181–1186

    Article  Google Scholar 

  • Conroy MA, Polich J (2007) Normative variation of P3a and P3b from a large sample (N=120): Gender, topography, and response time. J Psychophysiol 21:22–32

    Article  Google Scholar 

  • Faller J, Müller-Putz G, Schmalstieg D, Pfurtscheller G (2010) An application framework for controlling an avatar in a desktop based virtual environment via a software SSVEP brain-computer interface. Presence: Teleoperators and Virtual Environments 19(1):25–34

    Article  Google Scholar 

  • Farwell LA, Donchin E (1988) Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70:510–523

    Article  Google Scholar 

  • Ferrez PW, Millán Jdel R (2008) Error-related EEG potentials generated during simulated brain-computer interaction. IEEE Trans Biomed Eng 55(3):923–929

    Article  Google Scholar 

  • Friedrich EVC, McFarland DJ, Neuper C, Vaughan TM, Brunner P, Wolpaw JR (2009) A scanning protocol for sensorimotor rhythm-based brain-computer interface. Biol Psychol 80:169–175

    Article  Google Scholar 

  • Gonsalvez CJ, Polich J (2002) P300 amplitude is determined by target-to-target interval. Psychophysiology 39(3):388–396

    Article  Google Scholar 

  • 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 Trans Neural Syst Rehabil Eng 11:145–147

    Article  Google Scholar 

  • Guger C, Daban S, Sellers E, Holzner C, Krausz G, Carabalona R, Gramatica F, Edlinger G (2009) How many people are able to control a P300-based brain-computer interface (BCI)? Neurosci Lett 462(1):94–8

    Article  Google Scholar 

  • Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099):164–171

    Article  Google Scholar 

  • Jing J, Allison BZ, Brunner C, Wang B, Wang X, Pfurtscheller G (2010) P300 Chinese input system based on PSO-LDA. Biomed Eng 55(1):5–18

    Article  Google Scholar 

  • Kübler A, Neumann N, Kaiser J, Kotchoubey B, Hinterberger T, Birbaumer N (2001) Brain computer communication: Self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil 82:1533–1539

    Article  Google Scholar 

  • Kübler A, Müller K-R (2007) Toward brain-computer interfacing. In: An Introduction to Brain-Computer Interfacing. MIT Press, Boston, pp 1–25

    Google Scholar 

  • Kübler A, Birbaumer N (2008) Brain-computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol 119(11):2658–2666

    Article  Google Scholar 

  • Kübler A, Furdea A, Halder S, Hammer EM, Nijboer F, Kotchoubey B (2009) A brain-computer interface controlled auditory event-related potential (P300) spelling system for locked-in patients. Ann NY Acad Sci 1157:90–100

    Article  Google Scholar 

  • Leeb R, Lee F, Keinrath C, Scherer R, Bischof H, Pfurtscheller G (2007) Brain-computer communication: Motivation, aim and impact of exploring a virtual apartment. IEEE Trans Neural Syst Rehabil Eng 15:473–482

    Article  Google Scholar 

  • Lenhardt A, Kaper M, Ritter HJ (2008) An adaptive P300-based online brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 16(2):121–130

    Article  Google Scholar 

  • Mason SG, Bashashati A, Fatourechi M, Navarro KF, Birch GE (2007) A comprehensive survey of brain interface technology designs. Ann Biomed Eng 35:137–169

    Article  Google Scholar 

  • Millán Jdel R, Mouriño J (2003) Asynchronous BCI and local neural classifiers: An overview of the Adaptive Brain Interface project. IEEE Trans Neural Syst Rehabil Eng 11(2):159–161

    Article  Google Scholar 

  • Müller-Putz GR, Scherer R, Neuper C, Pfurtscheller G (2006) Steady-state somatosensory evoked potentials: Suitable brain signals for brain-computer interfaces? IEEE Trans Neural Syst Rehabil Eng 14(1):30 –37

    Article  Google Scholar 

  • Müller-Putz GR, Scherer R, Brunner C, Leeb R, Pfurtscheller G (2008) Better than random? A closer look on BCI results. Int J Bioelectromagn 10:52–55

    Google Scholar 

  • Neuper C, Pfurtscheller G (in press) Neurofeedback training for BCI control. In: Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. Graimann B, Allison BZ, Pfurtscheller G (eds) Springer, Berlin

    Google Scholar 

  • Neuper C, Scherer R, Reiner M, Pfurtscheller G (2005) Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Res Cogn Brain Res 25(3):668–677

    Article  Google Scholar 

  • Nijboer F, Broermann U (in press) Brain-computer interfaces for communication and control in locked-in patients. Toward ubiquitous BCIs. In: Graimann B, Allison BZ, Pfurtscheller G (eds) Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. Springer, Berlin

    Google Scholar 

  • Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kübler A (2008) An auditory brain-computer interface (BCI). J Neurosci Methods 167(1):43–50

    Article  Google Scholar 

  • Nijholt A, Tan D, Pfurtscheller G, Brunner C, Millán JR, Allison BZ, Graimann B, Popescu F, Blankertz B, Müller K-R (2008) Brain-computer interfacing for intelligent systems. IEEE Intell Syst 23:72–79

    Article  Google Scholar 

  • Nikulin VV, Hohlefeld FU, Jacobs AM, Curio G (2008) Quasi-movements: A novel motor-cognitive phenomenon. Neuropsychologia 46(2):727–742

    Article  Google Scholar 

  • Perelmouter J, Birbaumer N (2000) A binary spelling interface with random errors. IEEE Trans Rehabil Eng 8:227–232

    Article  Google Scholar 

  • Pfurtscheller G, Neuper C (in press) Dynamics of sensorimotor oscillations in a motor task. In: Graimann B, Allison BZ, Pfurtscheller G (eds) Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. Springer, Berlin

    Google Scholar 

  • Pfurtscheller G, Flotzinger D, Pregenzer M, Wolpaw JR, McFarland D (1996) EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components. Med Prog Technol 21(3):111–121

    Google Scholar 

  • Pfurtscheller G, Neuper C, Guger C, Harkam W, Ramoser H, Schlögl A, Obermaier B, Pregenzer M (2000) Current trends in Graz Brain-Computer Interface (BCI) research. IEEE Trans Rehabil Eng 8(2):216–219

    Article  Google Scholar 

  • Pfurtscheller G, Müller-Putz GR, Schlögl A, Graimann B, Scherer R, Leeb R, Brunner C, Keinrath C, Lee F, Townsend G, Vidaurre C, Neuper C (2006) 15 years of BCI research at Graz University of Technology: Current projects. IEEE Trans Neural Syst Rehabil Eng 14:205–210

    Article  Google Scholar 

  • Pfurtscheller G, Müller-Putz GR, Scherer R, Neuper C (2008) Rehabilitation with brain-computer interface systems. IEEE Comput Mag 41:58–65

    Article  Google Scholar 

  • Polich J (1986) Normal variation of P300 from auditory stimuli. Electroencephalogr Clin Neurophysiol 65:236–240

    Article  Google Scholar 

  • Popescu F, Fazli S, Badower Y, Blankertz B, Müller K-R (2007) Single trial classification of motor imagination using 6 dry EEG electrodes. PLoS One 2(7):e637

    Article  Google Scholar 

  • Schalk G, Wolpaw JR, McFarland DJ, Pfurtscheller G (2000) EEG-based communication: Presence of an error potential. Clin Neurophysiol 111(12):2138–2144

    Article  Google Scholar 

  • Schalk G, Miller KJ, Anderson NR, Wilson JA, Smyth MD, Ojemann JG, Moran DW, Wolpaw JR, Leuthardt EC (2008) Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng 5(1):75–84

    Article  Google Scholar 

  • Scherer R, Müller GR, Neuper C, Graimann B, Pfurtscheller G (2004) An asynchronously controlled EEG-based virtual keyboard: Improvement of the spelling rate. IEEE Trans Neural Syst Rehabil Eng 51:979–984

    Google Scholar 

  • Scherer R, Lee F, Schlögl A, Leeb R, Bischof H, Pfurtscheller G (2008) Toward self-paced brain-computer communication: Navigation through virtual worlds. IEEE Trans Biomed Eng 55(2):675–682

    Article  Google Scholar 

  • Sellers EW, Donchin E (2006) A P300-based brain-computer interface: Initial tests by ALS patients. Clin Neurophysiol 117(3):538–548

    Article  Google Scholar 

  • Sullivan TJ, Deiss SR, Jung T-P, Cauwenberghs G (2008) A brain-machine interface using dry-contact, low-noise EEG sensors. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS’2008), Seattle, USA, pp 1986–1989

    Google Scholar 

  • Wang Y, Wang R, Gao X, Hong B, Gao S (2006) A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 14:234–240

    Article  Google Scholar 

  • Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791

    Article  Google Scholar 

  • Wolpaw JR, Loeb GE, Allison BZ, Donchin E, do Nascimento OF, Heetderks WJ, Nijboer F, Shain WG, Turner JN (2006) BCI meeting 2005—Workshop on signals and recording methods. IEEE Trans Neural Syst Rehabil Eng 14:138–141

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by two grants: the Information and Communication Technologies Coordination and Support action “FutureBNCI”, Project number ICT-2010-248320; and the Information and Communication Technologies Collaborative Project action “BrainAble”, Project number ICT-2010-247447. We are grateful to Dr. Florin Popescu for suggesting the term “proficiency” as an alternative to “literacy”, and to Prof. Dr. John Polich, Prof. Dr. Andrea Kübler, Dr. Femke Nijboer, and Dr. Günter Krausz for comments. We thank Dr. Clemens Brunner for help with Figs. 3.13.3, and Dr. Jin Jing for providing Fig. 3.4. We also thank Josef Faller for help with formatting, and we thank an anonymous reviewer for insightful comments.

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Correspondence to Brendan Z. Allison .

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Allison, B.Z., Neuper, C. (2010). Could Anyone Use a BCI?. In: Tan, D., Nijholt, A. (eds) Brain-Computer Interfaces. Human-Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-84996-272-8_3

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  • DOI: https://doi.org/10.1007/978-1-84996-272-8_3

  • Publisher Name: Springer, London

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