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Towards Practical Brain-Computer Interfaces pp 375–393Cite as

Non-visual and Multisensory BCI Systems: Present and Future

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

Abstract

During the past decade, brain–computer interfaces (BCIs) have rapidly developed, both in technological and application domains. However, most of these interfaces rely on the visual modality. Only some research groups have been studying non-visual BCIs, primarily based on auditory and, sometimes, on somatosensory signals. These non-visual BCI approaches are especially useful for severely disabled patients with poor vision. From a broader perspective, multisensory BCIs may offer more versatile and user-friendly paradigms for control and feedback. This chapter describes current systems that are used within auditory and somatosensory BCI research. Four categories of noninvasive BCI paradigms are employed: (1) P300 evoked potentials, (2) steady-state evoked potentials, (3) slow cortical potentials, and (4) mental tasks. Comparing visual and non-visual BCIs, we propose and discuss different possible multisensory combinations, as well as their pros and cons. We conclude by discussing potential future research directions of multisensory BCIs and related research questions

Keywords

  • Amyotrophic Lateral Sclerosis
  • Motor Imagery
  • Amyotrophic Lateral Sclerosis Patient
  • Mental Task
  • Oddball Paradigm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Allison, B., McFarland, D., Schalk, G., Zheng, S., Jackson, M., Wolpaw, J.: Towards an independent brain–computer interface using steady state visual evoked potentials. Clin. Neurophysiol. 119(2), 399–408 (2008)

    Google Scholar 

  2. Allison, B.Z., Pineda, J.A.: ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 110–113 (2003). DOI 10.1109/TNSRE.2003.814448

    Google Scholar 

  3. Belitski, A., Farquhar, J., Desain, P.: P300 audio–visual speller. J. Neural Eng. 8(2), 025,022 (2011). DOI 10.1088/1741-2560/8/2/025022, http://dx.doi.org/10.1088/1741-2560/8/2/025022

    Google Scholar 

  4. Birbaumer, N., Hinterberger, T., Kübler, A., Neumann, N.: The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 120–123 (2003). DOI 10.1109/TNSRE.2003.814439, http://dx.doi.org/10.1109/TNSRE.2003.814439

    Google Scholar 

  5. Bregman, A.: Auditory scene analysis: Hearing in complex environments. In: McAdams, S., Bigand, E. (eds.) Thinking in sound: the cognitive psychology of human audition, pp. 10–36. Oxford University Press, Oxford (1993)

    Google Scholar 

  6. Brouwer, A.M., van Erp, J.B.: A tactile P300 brain–computer interface. Front. Neurosci. 4, 19 (2010). DOI 10.3389/fnins.2010.00019

    Google Scholar 

  7. Brouwer, A.M., van Erp, J.B.F., Aloise, F., Cincotti, F.: Tactile, visual and bimodal P300s: Could bimodal P300s boost BCI performance? SRX Neuroscience, Article ID:967027

    Google Scholar 

  8. Brumberg, J.S., Wright, E.J., Andreasen, D.S., Guenther, F.H., Kennedy, P.R.: Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech-motor cortex. Front. Neurosci. 5, 65 (2011). DOI 10.3389/fnins.2011.00065, http://dx.doi.org/10.3389/fnins.2011.00065

  9. Cabrera, A., Dremstrup, K.: Auditory and spatial navigation imagery in brain–computer interface using optimized wavelets. J. Neurosci. Methods 174(1), 135–146 (2008)

    Google Scholar 

  10. Chatterjee, A., Aggarwal, V., Ramos, A., Acharya, S., Thakor, N.: A brain–computer interface with vibrotactile biofeedback for haptic information. J. Neuroeng. Rehabil. 4(1), 40 (2007)

    Google Scholar 

  11. Cincotti, F., Kauhanen, L., Aloise, F., Palomäki, T., Caporusso, N., Jylänki, P., Mattia, D., Babiloni, F., Vanacker, G., Nuttin, M., et al.: Vibrotactile feedback for brain–computer interface operation. Comput. Intell. Neurosci. 2007:48937 (2007)

    Google Scholar 

  12. Curran, E., Sykacek, P., Stokes, M., Roberts, S., Penny, W., Johnsrude, I., Owen, A.: Cognitive tasks for driving a brain–computer interfacing system: a pilot study. IEEE Trans. Neural Syst. Rehabil. Eng. 12(1), 48–54 (2004)

    Google Scholar 

  13. Daly, I., Nasuto, S., Warwick, K.: Towards natural human computer interaction in BCI. In: AISB 2008 Convention Communication, Interaction and Social Intelligence, vol 1, p. 26 (2008)

    Google Scholar 

  14. Desain, P., Hupse, A., Kallenberg, M., de Kruif, B., Schaefer, R.: Brain–computer interfacing using selective attention and frequency-tagged stimuli. In: Proceedings of the 3rd International Brain–Computer Interface Workshop & Training Course, Graz, Austria, pp. 98–99 (2006)

    Google Scholar 

  15. Farquhar, J., Blankespoor, J., Vlek, R., Desain, P.: Towards a noise-tagging auditory BCI-paradigm. In: Proceedings of the 4th International BCI Workshop and Training Course, Graz, Austria, pp. 50–55 (2008)

    Google Scholar 

  16. 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(6), 510–523 (1988)

    Google Scholar 

  17. Friedrich, E., Scherer, R., Neuper, C.: The effect of distinct mental strategies on classification performance for brain–computer interfaces. International J. Psychophysiol. (2012)

    Google Scholar 

  18. Furdea, A., Halder, S., Krusienski, D., Bross, D., Nijboer, F., Birbaumer, N., Kübler, A.: An auditory oddball (P300) spelling system for brain–computer interfaces. Psychophysiology 46(3), 617–625 (2009). DOI 10.1111/j.1469-8986.2008.00783.x

    Google Scholar 

  19. Ghazanfar, A., Schroeder, C.: Is neocortex essentially multisensory? Trends in Cognitive Sciences 10(6), 278–285 (2006)

    Google Scholar 

  20. Gomez-Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.: Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery. In: Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on IEEE, pp. 121–126 (2010)

    Google Scholar 

  21. Guo, J., Hong, B., Guo, F., Gao, X., Gao, S.: An auditory BCI using voluntary mental response. In: Neural Engineering, 2009. NER’09. 4th International IEEE/EMBS Conference on IEEE, pp. 455–458 (2009)

    Google Scholar 

  22. Guo, J., Gao, S., Hong, B.: An auditory brain–computer interface using active mental response. IEEE Trans. Neural Syst. Rehabil. Eng. 18(3), 230–235 (2010)

    Google Scholar 

  23. 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). DOI 10.1016/j.clinph.2009.11.087, http://dx.doi.org/10.1016/j.clinph.2009.11.087

  24. Hill, N., Lal, T., Bierig, K., Birbaumer, N., Schölkopf, B.: An auditory paradigm for brain–computer interfaces. Adv. Neural Inf. Process. Syst. 17, 569–76 (2005)

    Google Scholar 

  25. Hill, N.J., Schölkopf, B.: An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli. J Neural Eng. 9(2):026011 (2012)

    Google Scholar 

  26. Hinterberger, T.: The sensorium: a multimodal neurofeedback environment. Adv. Hum. Comput. Interact. 2011, 3 (2011)

    Google Scholar 

  27. Hinterberger, T., Hill, J., Birbaumer, N.: An auditory brain–computer communication device. In: Biomedical Circuits and Systems, 2004 IEEE International Workshop on IEEE, pp. S3–6 (2004a)

    Google Scholar 

  28. Hinterberger, T., Neumann, N., Pham, M., Kübler, A., Grether, A., Hofmayer, N., Wilhelm, B., Flor, H., Birbaumer, N.: A multimodal brain-based feedback and communication system. Exp. Brain Res. 154, 521–526 (2004b). DOI 10.1007/s00221-003-1690-3

    Google Scholar 

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

    Google Scholar 

  30. Hong, B., Lou, B., Guo, J., Gao, S.: Adaptive active auditory brain computer interface. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, IEEE, pp. 4531–4534 (2009)

    Google Scholar 

  31. Jacobs, L., Bozian, D., Heffner, R., Barron, S.: An eye movement disorder in amyotrophic lateral sclerosis. Neurology 31(10), 1282–1287 (1981)

    Google Scholar 

  32. Kanoh, S., Miyamoto, K., Yoshinobu, T.: A brain–computer interface (BCI) system based on auditory stream segregation. Conf Proc IEEE Eng Med Biol Soc., 2008:642–645 (2008)

    Google Scholar 

  33. Keil, A., Gruber, T., Müller, M., Moratti, S., Stolarova, M., Bradley, M., Lang, P.: Early modulation of visual perception by emotional arousal: evidence from steady-state visual evoked brain potentials. Cogn. Affect. Behav. Neurosci. 3(3), 195–206 (2003)

    Google Scholar 

  34. Kelly, S., Lalor, E., Finucane, C., McDarby, G., Reilly, R.: Visual spatial attention control in an independent brain–computer interface. IEEE Trans. Biomed. Eng. 52(9), 1588–1596 (2005)

    Google Scholar 

  35. Kim, D.W., Hwang, H.J., Lim, J.H., Lee, Y.H., Jung, K.Y., Im, C.H.: Classification of selective attention to auditory stimuli: toward vision-free brain–computer interfacing. J. Neurosci. Methods 197(1), 180–185 (2011). DOI 10.1016/j.jneumeth.2011.02.007, http://dx.doi.org/10.1016/j.jneumeth.2011.02.007

    Google Scholar 

  36. Klobassa, D.S., Vaughan, T.M., Brunner, P., Schwartz, N.E., Wolpaw, J.R., Neuper, C., Sellers, E.W.: Toward a high-throughput auditory P300-based brain–computer interface. Clin. Neurophysiol. 120(7), 1252–1261 (2009). DOI 10.1016/j.clinph.2009.04.019, http://dx.doi.org/10.1016/j.clinph.2009.04.019

    Google Scholar 

  37. Klonowski, W., Duch, W., Perovic, A., Jovanovic, A.: Some computational aspects of the brain computer interfaces based on inner music. Comput. Intell. Neurosci. 2009:950403 (2009)

    Google Scholar 

  38. de Kruif, B., Schaefer, R., Desain, P.: Classification of imagined beats for use in a brain computer interface. Conf Proc IEEE Eng Med Biol Soc., 2007:678–681 (2007)

    Google Scholar 

  39. Kübler, A., Furdea, A., Halder, S., Hammer, E., 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). DOI 10.1111/j.1749-6632.2008.04122.x

    Google Scholar 

  40. Lopez, M., Pomares, H., Pelayo, F., Urquiza, J., Perez, J.: Evidences of cognitive effects over auditory steady-state responses by means of artificial neural networks and its use in brain–computer interfaces. Neurocomputing 72(16-18), 3617–3623 (2009)

    Google Scholar 

  41. Lulé D., Diekmann, V., Müller, H., Kassubek, J., Ludolph, A., Birbaumer, N.: Neuroimaging of multimodal sensory stimulation in amyotrophic lateral sclerosis. J. Neurol. Neurosurg. Psychiatry 81(8), 899 (2010)

    Google Scholar 

  42. McCorry, D.: Using statistical classification algorithms to decode covert speech states with functional magnetic resonance imaging. PhD thesis, George Mason University (2010)

    Google Scholar 

  43. Miranda, E.: Brain–computer music interface for composition and performance. Int. J. Disabil. Hum. Dev. 5(2), 119 (2006)

    Google Scholar 

  44. Miranda, E., Magee, W., Wilson, J., Eaton, J., Palaniappan, R.: Brain–computer music interfacing (BCMI): From basic research to the real world of special needs. Music Med. 3:134–140 (2011)

    Google Scholar 

  45. Mitchell, T., Shinkareva, S., Carlson, A., Chang, K.M., Malave, V., Mason, R., Just, M.: Predicting human brain activity associated with the meanings of nouns. Science 320(5880), 1191–1195 (2008). DOI 10.1126/science.1152876, http://dx.doi.org/10.1126/science.1152876

    Google Scholar 

  46. Mitsumoto, H., Przedborski, S., Gordon, P. (eds.): Amyotrophic Lateral Sclerosis. Taylor & Francis Group: New York, NY (2006)

    Google Scholar 

  47. Molina, G., Tsoneva, T., Nijholt, A.: Emotional brain–computer interfaces. In: Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on IEEE, pp. 1–9 (2009)

    Google Scholar 

  48. Müller-Putz, G., Neuper, C., Pfurtscheller, G.: Resonance-like frequencies of sensorimotor areas evoked by repetitive tactile stimulation. Biomed. Tech. (Berl.) 46, 186–190 (2001)

    Google Scholar 

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

    Google Scholar 

  50. Murphy, B., Poesio, M., Bovolo, F., Bruzzone, L., Dalponte, M., Lakany, H.: EEG decoding of semantic category reveals distributed representations for single concepts. Brain Lang. 117(1), 12–22 (2011). DOI 10.1016/j.bandl.2010.09.013, http://dx.doi.org/10.1016/j.bandl.2010.09.013

  51. Neuper, C., Pfurtscheller, G.: Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. Int. J. Psychophysiol. 43(1), 41–58 (2001)

    Google Scholar 

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

    Google Scholar 

  53. Pham, M., Hinterberger, T., Neumann, N., Kübler, A., Hofmayer, N., Grether, A., Wilhelm, B., Vatine, J., Birbaumer, N.: An auditory brain–computer interface based on the self-regulation of slow cortical potentials. Neurorehabil. Neural Repair 19(3), 206 (2005)

    Google Scholar 

  54. Polich, J.: Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118(10), 2128–2148 (2007). DOI 10.1016/j.clinph.2007.04.019, http://dx.doi.org/10.1016/j.clinph.2007.04.019

    Google Scholar 

  55. Porbadnigk, A., Wester, M., Calliess, J.P., Schultz, T.: EEG-based speech recognition – impact of temporal effects. In: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (2009)

    Google Scholar 

  56. Rosenboom, D.: Extended musical interface with the human nervous system. Leonardo Monograph Series International Society for the Arts, Sciences and Technology (ISAST) 1 (1997)

    Google Scholar 

  57. Roß B., Borgmann, C., Draganova, R., Roberts, L., Pantev, C.: A high-precision magnetoencephalographic study of human auditory steady-state responses to amplitude-modulated tones. J. Acoust. Soc. Am. 108, 679 (2000)

    Google Scholar 

  58. Rutkowski, T., Vialatte, F., Cichocki, A., Mandic, D., Barros, A.: Auditory feedback for brain computer interface management–an EEG data sonification approach. In: Knowledge-Based Intelligent Information and Engineering Systems, pp. 1232–1239. Springer-Verlag: Berling Heidelberg (2006)

    Google Scholar 

  59. Schreuder, M., Blankertz, B., Tangermann, M.: A new auditory multi-class brain–computer interface paradigm: spatial hearing as an informative cue. PLoS One 5, e9813 (2010). DOI 10.1371/journal.pone.0009813

    Google Scholar 

  60. Schröger, E., Widmann, A.: Speeded responses to audiovisual signal changes result from bimodal integration. Psychophysiology 35(6), 755–759 (1998). DOI 10.1111/1469-8986.3560755, http://dx.doi.org/10.1111/1469-8986.3560755

    Google Scholar 

  61. Sellers, E., Donchin, E.: A P300-based brain–computer interface: initial tests by ALS patients. Clin. Neurophysiol. 117(3), 538–548 (2006). DOI 10.1016/j.clinph.2005.06.027, http://dx.doi.org/10.1016/j.clinph.2005.06.027

  62. Sellers, E., Kübler, A., Donchin, E.: Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 speller. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 221–224 (2006). DOI 10.1109/TNSRE.2006.875580

    Google Scholar 

  63. Simanova, I., van Gerven, M., Oostenveld, R., Hagoort, P.: Identifying object categories from event-related EEG: toward decoding of conceptual representations. PLoS One 5(12), e14465 (2010). DOI 10.1371/journal.pone.0014465, http://dx.doi.org/10.1371/journal.pone.0014465

  64. Skrandies, W., Jedynak, A., Kleiser, R.: Scalp distribution components of brain activity evoked by visual motion stimuli. Exp. Brain Res. 122(1), 62–70 (1998)

    Google Scholar 

  65. Soto-Faraco, S., Väljamäe, A.: Multisensory interactions during motion perception: From basic principles to media applications. Taylor & Francis Group: New York, NY (2011)

    Google Scholar 

  66. Stapells, D., Herdman, A., Small, S., Dimitrijevic, A., Hatton, J.: Current status of the auditory steady-state responses for estimating an infant’s audiogram. A sound foundation through early amplification, pp. 43–59 (2004)

    Google Scholar 

  67. Suppes, P., Han, B., Lu, Z.L.: Brain wave recognition of words. Proc. Natl. Acad. Sci. USA 94(26), 14,965–14,969 (1997)

    Google Scholar 

  68. Suppes, P., Han, B., Lu, Z.L.: Brain-wave recognition of sentences. Proc. Natl. Acad. Sci. USA 95(26), 15,861–15,866 (1998)

    Google Scholar 

  69. Suppes, P., Han, B., Epelboim, J., Lu, Z.: Invariance between subjects of brain wave representations of language. Proc. Natl. Acad. Sci. 96(22), 12,953 (1999)

    Google Scholar 

  70. Sutton, S., Braren, M., Zubin, J., John, E.: Evoked-potential correlates of stimulus uncertainty. Science 150(700), 1187–1188 (1965)

    Google Scholar 

  71. Townsend, G., LaPallo, B., Boulay, C., Krusienski, D., Frye, G., Hauser, C., Schwartz, N., Vaughan, T., Wolpaw, J., Sellers, E.: A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin. Neurophysiol. 121, 1109–1120 (2010). DOI 10.1016/j.clinph.2010.01.030

    Google Scholar 

  72. Väljamäe, A., Kleiner, M.: Spatial sound in auditory vision substitution systems. In: Audio Engineering Society Convention, pp. 120 (2006). http://www.aes.org/e-lib/browse.cfm?elib=13599

  73. Väljamäe, A., Tajadura-Jimenez, A., Larsson, P., Västfjäll, D., Kleiner, M.: Handheld experiences: Using audio to enhance the illusion of self-motion. IEEE MultiMedia, pp. 68–75 (2008)

    Google Scholar 

  74. Vlek, R., Schaefer, R., Gielen, C., Farquhar, J., Desain, P.: Sequenced subjective accents for brain–computer interfaces. J. Neural Eng. 8(3), 036,002 (2011). DOI 10.1088/1741-2560/8/3/036002, http://dx.doi.org/10.1088/1741-2560/8/3/036002

    Google Scholar 

  75. Wagner, I.: An auditory brain–computer interface for binary choices using event-related potentials and lateralized hemispheric brain activity: Tests with healthy controls. Master Thesis, University of Graz, Graz, Austria (2011)

    Google Scholar 

  76. Wang, Y., Gao, X., Hong, B., Jia, C., Gao, S.: Brain–computer interfaces based on visual evoked potentials. IEEE Eng. Med. Biol. Mag. 27(5), 64–71 (2008)

    Google Scholar 

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

    Google Scholar 

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Acknowledgements

This work was supported by Support action “FutureBNCI,” Project number ICT-2010-248320.

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  1. Institute for Knowledge Discovery, BCI-Lab, Graz University of Technology, Graz, Austria

    Isabella C. Wagner, Ian Daly & Aleksander Väljamäe

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  1. Isabella C. Wagner
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    Brendan Z. Allison

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

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    José Del R. Millán

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    Anton Nijholt

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Wagner, I.C., Daly, I., Väljamäe, A. (2012). Non-visual and Multisensory BCI Systems: Present and Future. 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_19

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