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Designing Future BCIs: Beyond the Bit Rate

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Towards Practical Brain-Computer Interfaces

Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

The last 20 years of success in BCI design have led to the realisation of basic control functions by engineers, psychologists, machine learners and end users. These basic functions provide us with the freedom to design future BCI applications that go beyond the reliability of isolated intention detection events. Such a design process for the overall system comprises finding a suitable control metaphor, respecting neuroergonomic principles, designing visually aesthetic feedback, dealing with the learnability of the system, creating an effective application structure (navigation), and exploring the power of social aspects of an interactive BCI system.Designing a human-machine system also involves eliciting a user’s knowledge, preferences, requirements and priorities. In order not to overload end users with evaluation tasks and to take into account issues specific to BCI, techniques and processes from other fields that aim to acquire these must be adapted for applications that use BCI. We present examples which illustrate this process.

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References

  1. Allison, B.Z., Pineda, J.A.: Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system. Int. J. Psychophysiol. 59(2), 127–140 (2006)

    Google Scholar 

  2. Babiloni, F., Cincotti, F., Lazzarini, L., Millan, J., Mourinõ, J., Varsta, M., Heikkonen, J., Bianchi, L., Marciani, M.G.: Linear classification of low-resolution EEG patterns produced by imagined hand movements. IEEE Trans. Rehabil. Eng. 8(2), 186–188 (2000)

    Google Scholar 

  3. Ball, S., Rousell, J.: Virtual Disability: Simulations as an Aid to Lecturers’ Understanding of Disability. Computers Helping People with Special Needs, pp. 624–624 (2004)

    Google Scholar 

  4. Bensch, M., Karim, A., Mellinger, J., Hinterberger, T., Tangermann, M., Bogdan, M., Rosenstiel, W., Birbaumer, N.: Nessi: an EEG-controlled web browser for severely paralyzed patients. Comp. Intell. Neurosci. 2007, 71,863 (2007)

    Google Scholar 

  5. Biswas, P., Robinson, P.: Simulation to predict performance of assistive interfaces. In: Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility, ACM, pp. 227–228 (2007)

    Google Scholar 

  6. Biswas, P., Robinson, P.: Automatic evaluation of assistive interfaces. In: Proceedings of the 13th International Conference on Intelligent User Interfaces, ACM, pp. 247–256 (2008)

    Google Scholar 

  7. Blankertz, B., Dornhege, G., Schäfer, C., Krepki, R., Kohlmorgen, J., Müller, K.R., Kunzmann, V., FLosch, Curio, G.: Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 127–131 (2003)

    Google Scholar 

  8. Blankertz, B., Lemm, S., Treder, M.S., Haufe, S., Müller, K.R.: Single-trial analysis and classification of ERP components – a tutorial. Neuroimage 56, 814–825 (2011)

    Google Scholar 

  9. Burgstahler, S., Doe, T.: Disability-related simulations: If, when, and how to use them in professional development. Rev. Disabil. Stud. 1(2), 4–17 (2004)

    Google Scholar 

  10. Carlson, T., Demiris, Y.: Human-wheelchair collaboration through prediction of intention and adaptive assistance. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 3926–3931 (2008)

    Google Scholar 

  11. Chatterjee, A., Aggarwal, V., Ramos, A., Acharya, S., Thakor, N.: A brain–computer interface with vibrotactile biofeedback for haptic information. J. Neuroeng. Rehabil. 4, 40 (2007)

    Google Scholar 

  12. Cincotti, F., Kauhanen, L., Aloise, F., Palomaki, T., Caporusso, N., Jylanki, P., Mattia, D., Babiloni, F., Vanacker, G., Nuttin, M., Marciani, M.G., del R Millán, J.: Preliminary experimentation on vibrotactile feedback in the context of mu-rhythm based BCI. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 4739–4742 (2007a)

    Google Scholar 

  13. Cincotti, F., Kauhanen, L., Aloise, F., Palomäki, T., Caporusso, N., Jylänki, P., Mattia, D., Babiloni, F., Vanacker, G., Nuttin, M., Marciani, M.G., del R Millán, J.: Vibrotactile feedback for brain–computer interface operation. Intell. Neurosci. 2007, 7–7 (2007b)

    Google Scholar 

  14. Curran, A., Stokes, M.J.: Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain Cogn. 51(3), 326–336 (2003)

    Google Scholar 

  15. Dix, A., Finlay, J., Abowd, G., Beale, R.: Human–computer interaction. Prentice Hall, Hemel Hempstead (2004)

    Google Scholar 

  16. Fait, H., Mankoff, J.: EASE: A Simulation Tool for Accessible Design. Computer Science Division, University of California (2003)

    Google Scholar 

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

    Google Scholar 

  18. Fazli, S., Danóczy, M., Popescu, F., Blankertz, B., Müller, K.R.: Using rest class and control paradigms for brain computer interfacing. In: Proc. of the 10th Int. Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence, Heidelberg, IWANN ’09, pp. 651–665. Springer, Berlin (2009)

    Google Scholar 

  19. Felton, E.A., Radwin, R.G., Wilson, J.A., Williams, J.C.: Evaluation of a modified fitts law brain–computer interface target acquisition task in able and motor disabled individuals. J. Neural Eng. 6(5), 056,002 (2009)

    Google Scholar 

  20. Felzer, T., Strah, B., Nordmann, R.: Automatic and self-paced scanning for alternative text entry. In: Proc. IASTED Int. Conf. on Telehealth/Assistive Technologies, Telehealth/AT ’08, pp. 1–6. ACTA, Anaheim, CA, USA (2008)

    Google Scholar 

  21. Felzer, T., Nordmann, R., Rinderknecht, S.: Scanning-based human–computer interaction using intentional muscle contractions. Universal Access in Human–Computer Interaction Intelligent and Ubiquitous Interaction Environments, pp. 509–518 (2009)

    Google Scholar 

  22. Flemisch, O., Adams, A., Conway, S., Goodrich, K., Palmer, M., Schutte, P.: The H-Metaphor as a guideline for vehicle automation and interaction. Tech. Rep. NASA/TM–2003-212672, NASA (2003)

    Google Scholar 

  23. Gajos, K.Z., Everitt, K., Tan, D.S., Czerwinski, M., Weld, D.S.: Predictability and accuracy in adaptive user interfaces. In: Proc. of 26th annual SIGCHI conference on Human factors in computing systems, CHI ’08, pp. 1271–1274. ACM, New York, NY, USA (2008)

    Google Scholar 

  24. Goodrich, K., Schutte, P., Flemisch, F., Williams, R.: Application of the H-mode, a design and interaction concept for highly automated vehicles, to aircraft. In: Proc. IEEE Digital Avionics Syst. Conf., pp. 1–13 (2006)

    Google Scholar 

  25. Guger, C., Schlogl, A., Neuper, C., Walterspacher, D., Strein, T., Pfurtscheller, G.: Rapid prototyping of an EEG-based brain–computer interface (BCI). IEEE Trans. Neural Syst. Rehabil. Eng. 9(1), 49–58 (2001)

    Google Scholar 

  26. Hailpern, J., Danilevsky, M., Harris, A., Karahalios, K., Dell, G., Hengst, J.: Aces: promoting empathy towards aphasia through language distortion emulation software. In: Proc. of the 2011 annual conference on Human factors in computing systems, CHI ’11, pp. 609–618. ACM, New York, NY, USA (2011)

    Google Scholar 

  27. Halder, S., Rea, M., Andreoni, R., Nijboer, F., Hammer, E.M., Kleih, S.C., Birbaumer, N., Kübler, A.: An auditory oddball brain–computer interface for binary choices. Clin. Neurophysiol. 121(4), 516–523 (2010)

    Google Scholar 

  28. Heger, D., Putze, F., Schultz, T.: Online workload recognition from EEG data during cognitive tests and human-machine interaction. In: Proc. 33rd annual German conference on Advances in artificial intelligence, KI’10, pp. 410–417. Springer, Berlin, Heidelberg (2010)

    Google Scholar 

  29. Hill, J., Farquhar, J., Martens, S., Bießmann, F., Schölkopf, B.: Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance. Adv. Neural Inf. Process. Syst. 21, 665–672 (2009)

    Google Scholar 

  30. Hill, N., Lal, T.N., Tangermann, M., Hinterberger, T., Widman, G., Elger, C.E., Schölkopf, B., Birbaumer, N.: Classifying event-related desynchronization in EEG, ECoG and MEG signals. In: Dornhege, G., d R Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.) Toward Brain–Computer Interfacing, pp. 235–260. MIT press, Cambridge, MA (2007)

    Google Scholar 

  31. Hitchcock, D., Taylor, A.: Simulation for Inclusion–true user centred design. In: Proceedings of International Conference on Inclusive Design, Royal College of Art, London, Citeseer (2003)

    Google Scholar 

  32. Hitchcock, D., Lockyer, S., Cook, S., Quigley, C.: Third age usability and safety–an ergonomics contribution to design. Int. J. Hum. Comput. Stud. 55(4), 635–643 (2001)

    Google Scholar 

  33. Höhne, J., Schreuder, M., Blankertz, B., Tangermann, M.: A novel 9-class auditory ERP paradigm driving a predictive text entry system. Front. Neuroprosthetics 99(5), (2011)

    Google Scholar 

  34. Houde, S., Hill, C.: What do prototypes prototype? Handbook Hum. Comput. Interact. 2, 367–381 (1997)

    Google Scholar 

  35. Johnson, J.: Designing with the Mind in Mind: a Simple Guide to Understanding User Interface Design Rules. Morgan Kaufmann, Burlington (2011)

    Google Scholar 

  36. Kübler, A., Kotchoubey, B., Kaiser, J., Wolpaw, J.P., Birbaumer, N.: Brain–computer communication: Unlocking the locked in. Psychol. Bull. 127(3), 358–375 (2001)

    Google Scholar 

  37. Kübler, A., Furdea, A., Halder, S., Hammer, E.M., Nijboer, F., Kotchoubey, B.: A brain–computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Ann. N. Y. Acad. Sci. 1157, 90–100 (2009)

    Google Scholar 

  38. Langer, E.J., Roth, J.: Heads I win, tails it’s chance: The illusion of control as a function of the sequence of outcomes in a purely chance task. J. Pers. Soc. Psychol. 32(6), 951–955 (1975)

    Google Scholar 

  39. Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain–computer communication: Motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 15(4), 473–482 (2007)

    Google Scholar 

  40. Lemm, S., Blankertz, B., Curio, G., Müller, K.R.: Spatio-spectral filters for improving classification of single trial EEG. IEEE Trans. Biomed. Eng. 52(9), 1541–1548 (2005)

    Google Scholar 

  41. Millán, J.: On the need for on-line learning in brain–computer interfaces. In: Proceedings of the International Joint Conference on Neural Networks, Budapest, Hungary, iDIAP-RR 03-30 (2004)

    Google Scholar 

  42. Millán JdR., Rupp, R., Mueller-Putz, G., Murray-Smith, R., Giugliemma, C., Tangermann, M., Vidaurre, C., Cincotti, F., Kübler, A., Leeb, R., Neuper, C., Müller, K.R., Mattia, D.: Combining brain–computer interfaces and assistive technologies: State-of-the-art and challenges. Front. Neurosci. 4 161 (2010)

    Google Scholar 

  43. Mönßinger, J., Halder, S., Kleih, S., Furdea, A., Raco, V., Hösle, A., Kübler, A.: Brain painting: first evaluation of a new brain–computer interface application with als-patients and healthy volunteers. Front. Neurosci. 4, 1–11 (2010)

    Google Scholar 

  44. Müller, K.R., Krauledat, M., Dornhege, G., Curio, G., Blankertz, B.: Machine learning and applications for brain–computer interfacing. In: Smith, M.J., Salvendy, G. (eds.) Human Interface, Part I, HCII 2007, LNCS, vol. 4557, pp. 705–714. Springer, Berlin, Heidelberg (2007)

    Google Scholar 

  45. Nielsen, J.: Usability Engineering. Academic Press, Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  46. Norman, D.: Emotional design. Basic Books, New York (2005)

    Google Scholar 

  47. Norman, K.L.: The Psychology of Menu Selection: Designing Cognitive Control at the Human/Computer Interface. Greenwood, Westport, CT, USA (1991)

    Google Scholar 

  48. Obermaier, B., Neuper, C., Guger, C., Pfurtscheller, G.: Information transfer rate in a five-classes brain–computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 9(3), 283–288 (2001). DOI 10.1109/7333.948456

    Google Scholar 

  49. Patel, S.H., Azzam, P.N.: Characterization of N200 and P300: selected studies of the Event-Related Potential. Int. J. Med. Sci. 2, 147–154 (2005)

    Google Scholar 

  50. Plass-Oude Bos, D., Poel, M., Nijholt, A.: A study in user centered design and evaluation of mental tasks for BCI. In: Lee, K.T., Tsai, W.H., Liao, H.Y.M., Chen, T., Hsieh, J.W., Tseng, C.C. (eds.) Proc. of the 17th International Multimedia Modeling Conference, MMM 2011, Taipei, Taiwan, Lecture Notes in Computer Science, vol. 6524, pp. 122–134. Springer, Berlin, (2011)

    Google Scholar 

  51. Polich, J.: Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118, 2128–2148 (2007)

    Google Scholar 

  52. Polich, J., Ellerson, P.C., Cohen, J.: P300, stimulus intensity, modality, and probability. Int. J. Psychophysiol. 23, 55–62 (1996)

    Google Scholar 

  53. Quek, M., Boland, D., Williamson, J., Murray-Smith, R., Tavella, M., Perdikis, S., Schreuder, M., Tangermann, M.: Simulating the feel of brain–computer interfaces for design, development and social interaction. In: Proc. of the 2011 annual conference on Human factors in computing systems, CHI ’11, pp. 25–28. ACM, New York, NY, USA (2011)

    Google Scholar 

  54. Ravden, D., Polich, J.: Habituation of P300 from visual stimuli. Int. J. Psychophysiol. 30(3), 359–365 (1998)

    Google Scholar 

  55. Schreuder, M., Tangermann, M., Blankertz, B.: Initial results of a high-speed spatial auditory BCI. Int. J. Bioelectromagn. 11(2), 105–109 (2009)

    Google Scholar 

  56. Schreuder, M., Blankertz, B., Tangermann, M.: A new auditory multi-class brain–computer interface paradigm: Spatial hearing as an informative cue. Plos One 5(4), e9813 (2010)

    Google Scholar 

  57. Schreuder, M.; Höhne, J.; Treder, M.; Blankertz, B.; Tangermann, M.; , Performance optimization of ERP-based BCIs using dynamic stopping. IEEE Eng. Med. Biol. Soc. (2011) pp.4580–4583

    Google Scholar 

  58. Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S.: Designing the User Interface: Strategies for Effective Human–Computer Interaction (5th edn.). Addison Wesley, Boston (2009)

    Google Scholar 

  59. Simpson, R.C., Koester, H.H.: Adaptive one-switch row-column scanning. IEEE Trans. Rehabil. Eng. 7(4), 464–473 (1999)

    Google Scholar 

  60. Strachan, S., Murray-Smith, R., O’Modhrain, S.: Bodyspace: inferring body pose for natural control of a music player. In: CHI ’07 extended abstracts on Human factors in computing systems, CHI EA ’07, pp. 2001–2006. ACM, New York, NY, USA (2007)

    Google Scholar 

  61. Szekely, P.: User interface prototyping: Tools and techniques. In: Software Engineering and Human–Computer Interaction, pp. 76–92. Springer, Heidelberg (1995)

    Google Scholar 

  62. Thimbleby, H.: Understanding User Centred Design (UCD) for People with Special Needs. Computers Helping People with Special Needs, pp. 1–17 (2008)

    Google Scholar 

  63. Tonin, L., Leeb, R., Tavella, M., Perdikis, S., del R Millán, J.: The role of shared-control in BCI-based telepresence. In: Proc. of 2010 IEEE International Conference on Systems, Man and Cybernetics (2010)

    Google Scholar 

  64. Townsend, G., Graimann, B., Pfurtscheller, G.: Continuous EEG classification during motor imagery-simulation of an asynchronous BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 12(2), 258–265 (2004)

    Google Scholar 

  65. Tractinsky, N., Hassenzahl, M.: Arguing for aesthetics in human–computer interaction. I-com 4(3/2005), 66–68 (2005)

    Google Scholar 

  66. Tractinsky, N., Katz, A., Ikar, D.: What is beautiful is usable. Interact. Comput. 13(2), 127–145 (2000)

    Google Scholar 

  67. Treder, M.S., Blankertz, B.: (C)overt attention and visual speller design in an ERP-based brain–computer interface. Behav. Brain Funct. 6, 28 (2010)

    Google Scholar 

  68. Vidaurre, C., Kawanabe, M., von Bünau, P., Blankertz, B., Müller, K.R.: Toward unsupervised adaptation of lda for brain–computer interfaces. IEEE Trans. Biomed. Eng. 58(3), 587–597 (2011a)

    Google Scholar 

  69. Vidaurre, C., Sannelli, C., Müller, K.R., Blankertz, B.: Machine-learning based co-adaptive calibration. Neural Comput. 23(3), 791–816 (2011b)

    Google Scholar 

  70. Virzi, R.A., Sokolov, J.L., Karis, D.: Usability problem identification using both low- and high-fidelity prototypes. In: Proc. of the SIGCHI conference on Human factors in computing systems: common ground, CHI ’96, pp. 236–243. ACM, New York, NY, USA (1996)

    Google Scholar 

  71. Williamson, J., Murray-Smith, R., Blankertz, B., Krauledat, M., Müller, K.R.: Designing for uncertain, asymmetric control: Interaction design for brain–computer interfaces. Int. J. Hum. Comput. Stud. 67(10), 827–841 (2009)

    Google Scholar 

  72. Wilson, A.: Sensor-and recognition-based input for interaction. In: Sears, A., Jacko, J. (eds.) The Human Computer Interaction Handbook, pp. 177–200. Lawrence Erlbaum Associates (2007)

    Google Scholar 

  73. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Google Scholar 

  74. Zhang, B., Wang, J., Fuhlbrigge, T.: A review of the commercial brain–computer interface technology from perspective of industrial robotics. In: 2010 IEEE International Conference on Automation and Logistics ICAL 2010, pp. 379–384 (2010)

    Google Scholar 

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Authors and Affiliations

  1. School of Computing Science, University of Glasgow, Glasgow, Scotland, UK

    Melissa Quek & Roderick Murray-Smith

  2. BBCI Lab, Berlin Institute of Technology, Berlin, Germany

    Johannes Höhne & Michael Tangermann

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  1. Melissa Quek
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  2. Johannes Höhne
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  3. Roderick Murray-Smith
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  4. Michael Tangermann
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Correspondence to Melissa Quek .

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Editors and Affiliations

  1. Baltimore Drive 5700Apt 167, La Mesa, 91942, California, USA

    Brendan Z. Allison

  2. Starlab Neuroscience Research, Teodor Roviralta 45, Barcelona, 08022, Spain

    Stephen Dunne

  3. , School of Engineering, Swiss Federal Institute of Technology, EPFL STI-CNBI, ELB 141, Station 11, Lausanne, 1015, Switzerland

    Robert Leeb

  4. , School of Engineering, Swiss Federal Institute of Technology, EPFL STI-CNBI, ELB 138. Station 11, Lausanne, 1015, Switzerland

    José Del R. Millán

  5. , Faculty EEMCS, University of Twente, Enschede, 7500, Netherlands

    Anton Nijholt

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Quek, M., Höhne, J., Murray-Smith, R., Tangermann, M. (2012). Designing Future BCIs: Beyond the Bit Rate. In: Allison, B., Dunne, S., Leeb, R., Del R. Millán, J., Nijholt, A. (eds) Towards Practical Brain-Computer Interfaces. Biological and Medical Physics, Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29746-5_9

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