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Brain–Computer Interfaces and Assistive Technology

  • Rüdiger RuppEmail author
  • Sonja C. Kleih
  • Robert Leeb
  • José del R. Millan
  • Andrea Kübler
  • Gernot R. Müller-Putz
Chapter
Part of the The International Library of Ethics, Law and Technology book series (ELTE, volume 12)

Abstract

Assistive technology (AT) supports individuals with motor, sensory, or cognitive disabilities in performing functions that might otherwise be difficult or impossible for them. In particular, individuals with severe motor impairments have a high need for assistive devices supporting access to information technologies, improving mobility, and restoring manipulation abilities. Established human–machine interfaces are dependent on the presence of a sufficient number of residual motor functions. Brain–Computer Interfaces (BCIs) are technical systems that provide a direct connection between the human brain and a computer and can serve as a user interface for the control of assistive devices. Historically, non-invasive BCIs were intended to provide basic communication skills to patients with locked-in syndrome. Since then BCI technology has evolved tremendously and nowadays BCIs are used as an alternative or additional control channel for many other applications. Among them are extended communication applications like accessing the internet or Brain Painting. Wheelchairs and telepresence robots can be navigated with the help of BCIs, and motor-imagery-based BCIs in particular are an attractive perspective for an intuitive neuroprosthesis control. The recent development of the hybrid BCI combining a BCI with other preserved control signals fits well in the user-centered design concept, since BCIs can be seamlessly integrated in traditional AT. Although current non-invasive BCIs are at the stage of entering people’s homes, they still cannot be operated by end-users alone. More home-based studies are needed to further improve the usability and reliability of BCIs and to better address specific needs and requirements of end-users.

Keywords

Spinal Cord Injury Amyotrophic Lateral Sclerosis Amyotrophic Lateral Sclerosis Patient Assistive Technology Functional Electrical Stimulation 
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.

References

  1. Allison, B.Z., R. Leeb, C. Brunner, G.R. Müller-Putz, G. Bauernfeind, J.W. Kelly, and C. Neuper. 2012. Toward smarter BCIs: Extending BCIs through hybridization and intelligent control. Journal of Neural Engineering 9(1): 013001. doi: 10.1088/1741-2560/9/1/013001.CrossRefGoogle Scholar
  2. Anderson, K.D. 2004. Targeting recovery: Priorities of the spinal cord-injured population. Journal of Neurotrauma 21(10): 1371–1383.CrossRefGoogle Scholar
  3. Bensch, M., A.A. Karim, J. Mellinger, T. Hinterberger, M. Tangermann, M. Bogdan, W. Rosenstiel, and N. Birbaumer. 2007. Nessi: An EEG-controlled web browser for severely paralyzed patients. Computational Intelligence and Neuroscience 71863. doi: 10.1155/2007/71863.
  4. Bentin, S., T. Allison, A. Puce, E. Perez, and G. McCarthy. 1996. Electrophysiological studies of face perception in humans. Journal of Cognitive Neuroscience 8(6): 551–565. doi: 10.1162/jocn.1996.8.6.551.CrossRefGoogle Scholar
  5. Birbaumer, N., N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor. 1999. A spelling device for the paralysed. Nature 808(6725): 297–298.CrossRefGoogle Scholar
  6. Blankertz, B., G. Dornhege, M. Krauledat, K.R. Müller, V. Kunzmann, F. Losch, and G. Curio. 2006. The Berlin brain-computer interface: EEG-based communication without subject training. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2): 147–152. doi: 10.1109/TNSRE.2006.875557.CrossRefGoogle Scholar
  7. Borenstein, J., and Y. Koren. 1991. The vector field histogram – Fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation 7(3): 278–288.CrossRefGoogle Scholar
  8. Bradberry, T.J., R.J. Gentili, and J.L. Contreras-Vidal. 2010. Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. Journal of Neuroscience 30(9): 3432–3437. doi: 10.1523/JNEUROSCI.6107-09.2010.CrossRefGoogle Scholar
  9. Carlson T., and Y. Demiris. 2008. Human-wheelchair collaboration through prediction of intention and adaptive assistance. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA.Google Scholar
  10. Carlson, T., and J.d.R. Millán. 2013. Brain-controlled wheelchairs: A robotic architecture. IEEE Robotics and Automation Magazine 20(1): 65–73.Google Scholar
  11. Cincotti, F., D. Mattia, F. Aloise, S. Bufalari, G. Schalk, G. Oriolo, A. Cherubini, M.G. Marciani, and F. Babiloni. 2008. Non-invasive brain-computer interface system: Towards its application as assistive technology. Brain Research Bulletin 75(6): 796–803. doi: 10.1016/j.brainresbull.2008.01.007.CrossRefGoogle Scholar
  12. Clauzel, G., B. Kaltner, C. Breitwieser, and G.R. Müller-Putz. 2012. Combining Hybrid BCI and signal quality monitoring to improve user experience. Paper presented at the 1st international DECODER workshop, Boulogne-Billancourt, France, 11 March 2012.Google Scholar
  13. Collinger, J.L., B. Wodlinger, J.E. Downey, W. Wang, E.C. Tyler-Kabara, D.J. Weber, A.J.C. McMorland, M. Velliste, M.L. Boninger, and A.B. Schwartz. 2013. High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet 381(9866): 557–564.CrossRefGoogle Scholar
  14. Crago, P.E., W.D. Memberg, M.K. Usey, M.W. Keith, R.F. Kirsch, G.J. Chapman, M.A. Katorgi, and E.J. Perreault. 1998. An elbow extension neuroprosthesis for individuals with tetraplegia. IEEE Transactions on Rehabilitation Engineering 6(1): 1–6.CrossRefGoogle Scholar
  15. Creasey, G.H., K.L. Kilgore, D.L. Brown-Triolo, J.E. Dahlberg, P.H. Peckham, and M.W. Keith. 2000. Reduction of costs of disability using neuroprostheses. Assistive Technology 12(1): 67–75. doi: 10.1080/10400435.2000.10132010.CrossRefGoogle Scholar
  16. Damböck, D., M. Kienle, K. Bengler, and H. Bubb. 2011. The H-metaphor as an example for cooperative vehicle driving. Paper presented at the Proceedings of the 14th international conference on Human-computer interaction: Towards mobile and intelligent interaction environments – Volume Part III, Orlando, FL.Google Scholar
  17. Dietz, V., and A. Curt. 2006. Neurological aspects of spinal-cord repair: Promises and challenges. Lancet Neurology 5(8): 688–694. doi:S1474-4422(06)70522-1.CrossRefGoogle Scholar
  18. Eimer, M. 2000. Event-related brain potentials distinguish processing stages involved in face perception and recognition. Clinical Neurophysiology 111(4): 694–705. doi:S1388-2457(99)00285-0.CrossRefGoogle Scholar
  19. Enzinger, C., S. Ropele, F. Fazekas, M. Loitfelder, F. Gorani, T. Seifert, G. Reiter, C. Neuper, G. Pfurtscheller, and G.R. Müller-Putz. 2008. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Experimental Brain Research 190(2): 215–223. doi: 10.1007/s00221-008-1465-y.CrossRefGoogle Scholar
  20. Farwell, L.A., and E. Donchin. 1988. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70(6): 510–523.CrossRefGoogle Scholar
  21. Flemisch, O., A. Adams, S.R. Conway, K.H. Goodrich, M.T. Palmer, and P.C. Schutte. 2003. The H-metaphor as a guideline for vehicle automation and interaction. Hampton: NASA.Google Scholar
  22. Furdea, A., S. Halder, D.J. Krusienski, D. Bross, F. Nijboer, N. Birbaumer, and A. Kübler. 2009. An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology 46(3): 617–625. doi: 10.1111/j.1469-8986.2008.00783.x PSYP783.CrossRefGoogle Scholar
  23. Galán, F., M. Nuttin, E. Lew, P.W. Ferrez, G. Vanacker, J. Philips, and J.d.R. Millán. 2008. A brain-actuated wheelchair: Asynchronous and non-invasive brain-computer interfaces for continuous control of robots. Clinical Neurophysiology 119(9): 2159–2169.Google Scholar
  24. Gan, L.S., and A. Prochazka. 2010. Properties of the stimulus router system; a novel neural prosthesis. IEEE Transactions on Biomedical Engineering 57(2): 450–459. doi: 10.1109/TBME.2009.2031427.CrossRefGoogle Scholar
  25. Ganzini, L., W.S. Johnston, and W.F. Hoffman. 1999. Correlates of suffering in amyotrophic lateral sclerosis. Neurology 52(7): 1434–1440.CrossRefGoogle Scholar
  26. Gollee, H., I. Volosyak, A.J. McLachlan, K.J. Hunt, and A. Gräser. 2010. An SSVEP-based brain-computer interface for the control of functional electrical stimulation. IEEE Transactions on Bio-medical Engineering 57(8): 1847–1855. doi: 10.1109/TBME.2010.2043432.CrossRefGoogle Scholar
  27. Gordon, T., and J. Mao. 1994. Muscle atrophy and procedures for training after spinal cord injury. Physical Therapy 74(1): 50–60.Google Scholar
  28. Gourab, K., and B.D. Schmit. 2010. Changes in movement-related beta-band EEG signals in human spinal cord injury. Clinical Neurophysiology 121(12): 2017–2023. doi: 10.1016/j.clinph.2010.05.012.CrossRefGoogle Scholar
  29. Guger, C., S. Daban, E. Sellers, C. Holzner, G. Krausz, R. Carabalona, F. Gramatica, and G. Edlinger. 2009. How many people are able to control a P300-based brain-computer interface (BCI)? Neuroscience Letters 462(1): 94–98. doi: 10.1016/j.neulet.2009.06.045S0304-3940(09)00819-2.CrossRefGoogle Scholar
  30. Halder, S., M. Rea, R. Andreoni, F. Nijboer, E.M. Hammer, S.C. Kleih, N. Birbaumer, and A. Kübler. 2010. An auditory oddball brain-computer interface for binary choices. Clinical Neurophysiology 121(4): 516–523. doi: 10.1016/j.clinph.2009.11.087 S1388-2457(09)00751-2.CrossRefGoogle Scholar
  31. Hart, R.L., K.L. Kilgore, and P.H. Peckham. 1998. A comparison between control methods for implanted FES hand-grasp systems. IEEE Transactions on Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society 6(2): 208–218.CrossRefGoogle Scholar
  32. Hayashi, H., and S. Kato. 1989. Total manifestations of amyotrophic lateral sclerosis. ALS in the totally locked-in state. Journal of the Neurological Sciences 93(1): 19–35.CrossRefGoogle Scholar
  33. Hentz, V.R., and C. Leclercq. 2002. Surgical rehabilitation of the upper limb. London/Edinburgh/New York: W.B. Saunders.Google Scholar
  34. Hill, N.J., D. Gupta, P. Brunner, A. Gunduz, M.A. Adamo, A. Ritaccio, and G. Schalk. 2012. Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. Journal of Visualized Experiments 64. doi: 10.3791/3993 3993.
  35. Hochberg, L.R., D. Bacher, B. Jarosiewicz, N.Y. Masse, J.D. Simeral, J. Vogel, S. Haddadin, J. Liu, S.S. Cash, P. van der Smagt, and J.P. Donoghue. 2012. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485(7398): 372–375. doi: 10.1038/nature11076.CrossRefGoogle Scholar
  36. Hoffmann, U., J.M. Vesin, T. Ebrahimi, and K. Diserens. 2008. An efficient P300-based brain-computer interface for disabled subjects. Journal of Neuroscience Methods 167(1): 115–125. doi:S0165-0270(07)00109-4.CrossRefGoogle Scholar
  37. Höhne, J., M. Schreuder, B. Blankertz, and M. Tangermann. 2011. A novel 9-class auditory ERP paradigm driving a predictive text entry system. Frontiers in Neuroscience 5: 99. doi: 10.3389/fnins.2011.00099.CrossRefGoogle Scholar
  38. Holz, E.M., T. Kaufmann, L. Desideri, M. Malavasi, E.J. Hoogerwerf, and A. Kübler. 2011. User centred design in BCI development. Biological and Medical Physics, Biomedical Engineering 2013: 22.Google Scholar
  39. Holz, E., L. Botrel, and A. Kübler. 2013. Bridging gaps: Long-term independent BCI home use by a locked-in end-user. Paper presented at the 4th workshop on Tools for Brain Computer Interaction (TOBI), Sion, Switzerland.Google Scholar
  40. Horki, P., C. Neuper, G. Pfurtscheller, and G.R. Müller-Putz. 2010. Asynchronous steady-state visual evoked potential based BCI control of a 2-DoF artificial upper limb. Biomedizinische Technik Biomedical Engineering 55(6): 367–374. doi: 10.1515/BMT.2010.044.CrossRefGoogle Scholar
  41. ISO. 2010. ISO 9241:2010 Ergonomics of human-system interaction Part 210: Human-centred design for interactive systems. Geneva, Switzerland.Google Scholar
  42. Iturrate, I., J.M. Antelis, A. Kübler, and J. Minguez. 2009. A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Transactions on Robotics 25(3): 614–627.CrossRefGoogle Scholar
  43. Kaufmann, T., S.M. Schulz, C. Grunzinger, and A. Kübler. 2011. Flashing characters with famous faces improves ERP-based brain-computer interface performance. Journal of Neural Engineering 8(5): 056016. doi: 10.1088/1741-2560/8/5/056016S1741-2560(11)91629-2.CrossRefGoogle Scholar
  44. Kaufmann, T., S.M. Schulz, A. Koblitz, G. Renner, C. Wessig, and A. Kübler. 2012. Face stimuli effectively prevent brain-computer interface inefficiency in patients with neurodegenerative disease. Clinical Neurophysiology. doi:S1388-2457(12)00735-3.Google Scholar
  45. Kaufmann, T., E.M. Holz, and A. Kübler. 2013. The importance of user-centred design in BCI development: A case study with a locked-in patient. Submission to 4th TOBI (Tools for Brain-Computer Interaction) workshop, Sion, Switzerland, 23–25 Jan 2013.Google Scholar
  46. Keith, M.W., and H. Hoyen. 2002. Indications and future directions for upper limb neuroprostheses in tetraplegic patients: A review. Hand Clinics 18(3): 519–528; viii.CrossRefGoogle Scholar
  47. Kern, H., U. Carraro, N. Adami, C. Hofer, S. Loefler, M. Vogelauer, W. Mayr, R. Rupp, and S. Zampieri. 2010. One year of home-based daily FES in complete lower motor neuron paraplegia: Recovery of tetanic contractility drives the structural improvements of denervated muscle. Neurological Research 32(1): 5–12. doi: 10.1179/174313209X385644.CrossRefGoogle Scholar
  48. Kilgore, K.L., M. Scherer, R. Bobblitt, J. Dettloff, D.M. Dombrowski, N. Godbold, J.W. Jatich, R. Morris, J.S. Penko, E.S. Schremp, and L.A. Cash. 2001. Neuroprosthesis consumers’ forum: Consumer priorities for research directions. Journal of Rehabilitation Research and Development 38(6): 655–660.Google Scholar
  49. Kilgore, K.L., P.H. Peckham, M.W. Keith, F.W. Montague, R.L. Hart, M.M. Gazdik, A.M. Bryden, S.A. Snyder, and T.G. Stage. 2003. Durability of implanted electrodes and leads in an upper-limb neuroprosthesis. Journal of Rehabilitation Research and Development 40(6): 457–468.CrossRefGoogle Scholar
  50. Kilgore, K.L., H.A. Hoyen, A.M. Bryden, R.L. Hart, M.W. Keith, and P.H. Peckham. 2008. An implanted upper-extremity neuroprosthesis using myoelectric control. Journal of Hand Surgery. American Volume 33(4): 539–550. doi:S0363-5023(08)00011-7.CrossRefGoogle Scholar
  51. Kleih, S.C., F. Nijboer, S. Halder, and A. Kübler. 2010. Motivation modulates the P300 amplitude during brain-computer interface use. Clinical Neurophysiology 121(7): 1023–1031. doi: 10.1016/j.clinph.2010.01.034S1388-2457(10)00077-5.CrossRefGoogle Scholar
  52. Kreilinger, A., V. Kaiser, C. Breitwieser, J. Williamson, C. Neuper, and G.R. Müller-Putz. 2011. Switching between manual control and brain-computer interface using long term and short term quality measures. Frontiers in Neuroscience 5: 147. doi: 10.3389/fnins.2011.00147.Google Scholar
  53. Lauer, R.T., P.H. Peckham, K.L. Kilgore, and W.J. Heetderks. 2000. Applications of cortical signals to neuroprosthetic control: A critical review. IEEE Transactions on Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society 8(2): 205–208.CrossRefGoogle Scholar
  54. Leeb, R., M. Gubler, M. Tavella, H. Miller, and J.d.R. Millán. 2010. On the road to a neuroprosthetic hand: A novel hand grasp orthosis based on functional electrical stimulation. In Conference proceedings: Annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society conference 2010, 146–149. doi: 10.1109/IEMBS.2010.5627412.
  55. Leeb, R., H. Sagha, R. Chavarriaga, and J.d.R. Millán. 2011. A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities. Journal of Neural Engineering 8(2): 025011. doi: 10.1088/1741-2560/8/2/025011.
  56. Leeb, R., S. Perdikis, L. Tonin, A. Biasiucci, M. Tavella, A. Molina, A. Al-Khodairy, T. Carlson, and J.d.R. Millán. 2013. Transferring brain-computer interface skills: From simple BCI training to successful application control. Artificial Intelligence in Medicine 59: 121–132.Google Scholar
  57. Loeb, G.E., and R. Davoodi. 2005. The functional reanimation of paralyzed limbs. IEEE Engineering in Medicine and Biology Magazine 24(5): 45–51.CrossRefGoogle Scholar
  58. Logroscino, G., B.J. Traynor, O. Hardiman, A. Chio, D. Mitchell, R.J. Swingler, A. Millul, E. Benn, and E. Beghi. 2010. Incidence of amyotrophic lateral sclerosis in Europe. Journal of Neurology, Neurosurgery and Psychiatry 81(4): 385–390. doi: 10.1136/jnnp.2009.183525.CrossRefGoogle Scholar
  59. Millán, J.d.R., F. Renkens, J. Mouriño, and W. Gerstner. 2004. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Transactions on Biomedical Engineering 51(6): 1026–1033.Google Scholar
  60. Millán, J.d.R., F. Galán, D. Vanhooydonck, E. Lew, J. Philips and M. Nuttin. 2009. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. In Proceedings of the 31st annual international conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis.Google Scholar
  61. Moss, C.W., K.L. Kilgore, and P.H. Peckham. 2011. A novel command signal for motor neuroprosthetic control. Neurorehabilitation and Neural Repair. doi:1545968311410067.Google Scholar
  62. Mugler, E.M., C.A. Ruf, S. Halder, M. Bensch, and A. Kübler. 2010. Design and implementation of a P300-based brain-computer interface for controlling an internet browser. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18(6): 599–609. doi: 10.1109/TNSRE.2010.2068059.CrossRefGoogle Scholar
  63. Mulcahey, M.J., B.T. Smith, and R.R. Betz. 1999. Evaluation of the lower motor neuron integrity of upper extremity muscles in high level spinal cord injury. Spinal Cord 37(8): 585–591.CrossRefGoogle Scholar
  64. Müller-Putz, G.R., R. Scherer, G. Pfurtscheller, and R. Rupp. 2005. EEG-based neuroprosthesis control: A step towards clinical practice. Neuroscience Letters 382(1–2): 169–174.CrossRefGoogle Scholar
  65. Müller-Putz, G.R., R. Scherer, C. Neuper, and G. Pfurtscheller. 2006. Steady-state somatosensory evoked potentials: Suitable brain signals for brain-computer interfaces? IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(1): 30–37. doi: 10.1109/TNSRE.2005.863842.CrossRefGoogle Scholar
  66. Müller-Putz, G.R., V. Kaiser, T. Solis-Escalante, and G. Pfurtscheller. 2010. Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG. Medical and Biological Engineering and Computing 48(3): 229–233. doi: 10.1007/s11517-009-0572-7.CrossRefGoogle Scholar
  67. Müller-Putz, G.R., C. Breitwieser, F. Cincotti, R. Leeb, M. Schreuder, F. Leotta, M. Tavella, L. Bianchi, A. Kreilinger, A. Ramsay, M. Rohm, M. Sagebaum, L. Tonin, C. Neuper, and J.d.R. Millán. 2011. Tools for brain-computer interaction: A general concept for a hybrid BCI. Frontiers in Neuroinformatics 5: 30. doi: 10.3389/fninf.2011.00030.
  68. Munssinger, J.I., S. Halder, S.C. Kleih, A. Furdea, V. Raco, A. Hosle, and A. Kübler. 2010. Brain painting: First evaluation of a new brain-computer interface application with ALS-patients and healthy volunteers. Frontiers in Neuroscience 4: 182. doi: 10.3389/fnins.2010.00182.CrossRefGoogle Scholar
  69. Murguialday, A.R., J. Hill, M. Bensch, S. Martens, S. Halder, F. Nijboer, B. Schoelkopf, N. Birbaumer, and A. Gharabaghi. 2011. Transition from the locked in to the completely locked-in state: A physiological analysis. Clinical Neurophysiology 122(5): 925–933. doi: 10.1016/j.clinph.2010.08.019 S1388-2457(10)00661-9.CrossRefGoogle Scholar
  70. Neuper, C., R. Scherer, M. Reiner, and G. Pfurtscheller. 2005. Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Research. Cognitive Brain Research 25(3): 668–677. doi: 10.1016/j.cogbrainres.2005.08.014.CrossRefGoogle Scholar
  71. Nijboer, F., E.W. Sellers, J. Mellinger, M.A. Jordan, T. Matuz, A. Furdea, S. Halder, U. Mochty, D.J. Krusienski, T.M. Vaughan, J.R. Wolpaw, N. Birbaumer, and A. Kübler. 2008. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology 119(8): 1909–1916. Available online: https://www.nscisc.uab.edu/reports.aspx. Last access 29th April 2014.
  72. NSCISC. 2011. The 2011 annual statistical report for the model spinal cord injury care system. National SCI Statistical Center. Available online: https://www.nscisc.uab.edu/reports.aspx. Last access 29th April 2014.
  73. Ofner, P., and G.R. Müller-Putz. 2012. Decoding of velocities and positions of 3D arm movement from EEG. In Conference proceedings: Annual international conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society conference 2012, 6406–6409. doi: 10.1109/EMBC.2012.6347460.
  74. Ortner, R., B.Z. Allison, G. Korisek, H. Gaggl, and G. Pfurtscheller. 2011. An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Transactions on Neural Systems and Rehabilitation Engineering 19(1): 1–5. doi: 10.1109/TNSRE.2010.2076364.CrossRefGoogle Scholar
  75. Ouzký, M. 2002. Towards concerted efforts for treating and curing spinal cord injury. Report of the Social, Health and Family Affairs Committee of the Council of Europe, Doc. 9401. Available under http://assembly.coe.int/ASP/Doc/XrefViewHTML.asp?FileID=9680&Language=en. Last access 29 Apr 2014.
  76. Peckham, P.H., M.W. Keith, K.L. Kilgore, J.H. Grill, K.S. Wuolle, G.B. Thrope, P. Gorman, J. Hobby, M.J. Mulcahey, S. Carroll, V.R. Hentz, and A. Wiegner. 2001. Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: a multicenter study. Archives of Physical Medicine and Rehabilitation 82(10): 1380–1388. doi:S0003-9993(01)45286-5.CrossRefGoogle Scholar
  77. Pfurtscheller, G., and F.H. Lopes da Silva. 1999. Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 110(11): 1842–1857.CrossRefGoogle Scholar
  78. Pfurtscheller, G., G.R. Müller, J. Pfurtscheller, H.J. Gerner, and R. Rupp. 2003a. ‘Thought’ – Control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience Letters 351(1): 33–36. doi:S0304394003009479.CrossRefGoogle Scholar
  79. Pfurtscheller, G., C. Neuper, G.R. Muller, B. Obermaier, G. Krausz, A. Schlogl, R. Scherer, B. Graimann, C. Keinrath, D. Skliris, M. Wortz, G. Supp, and C. Schrank. 2003b. Graz-BCI: State of the art and clinical applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2): 177–180. doi: 10.1109/TNSRE.2003.814454.CrossRefGoogle Scholar
  80. Pfurtscheller, G., P. Linortner, R. Winkler, G. Korisek, and G.R. Müller-Putz. 2009. Discrimination of motor imagery-induced EEG patterns in patients with complete spinal cord injury. Computational Intelligence and Neuroscience 104180. doi: 10.1155/2009/104180.
  81. Piccione, F., F. Giorgi, P. Tonin, K. Priftis, S. Giove, S. Silvoni, G. Palmas, and F. Beverina. 2006. P300-based brain computer interface: Reliability and performance in healthy and paralysed participants. Clinical Neurophysiology 117(3): 531. doi:S1388-2457(05)00459-1.CrossRefGoogle Scholar
  82. Pineda, J.A., D.S. Silverman, A. Vankov, and J. Hestenes. 2003. Learning to control brain rhythms: Making a brain-computer interface possible. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2): 181–184. doi: 10.1109/TNSRE.2003.814445.CrossRefGoogle Scholar
  83. Pokorny, C., D.S. Klobassa, G. Pichler, H. Erlbeck, R.G. Real, A. Kübler, D. Lesenfants, D. Habbal, Q. Noirhomme, M. Risetti, D. Mattia, and G.R. Müller-Putz. 2013. The auditory P300-based single-switch brain-computer interface: Paradigm transition from healthy subjects to minimally conscious patients. Artificial Intelligence in Medicine 59(2): 81–90. doi: 10.1016/j.artmed.2013.07.003.CrossRefGoogle Scholar
  84. Popovic, M.R., D.B. Popovic, and T. Keller. 2002. Neuroprostheses for grasping. Neurological Research 24(5): 443–452.CrossRefGoogle Scholar
  85. Rebsamen, B., C. Guan, H. Zhang, C. Wang, C. Teo, M.H. Ang, and E. Burdet. 2010. A brain controlled wheelchair to navigate in familiar environments. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18(6): 590–598.CrossRefGoogle Scholar
  86. Riccio, A., F. Leotta, L. Bianchi, F. Aloise, C. Zickler, E.J. Hoogerwerf, A. Kübler, D. Mattia, and F. Cincotti. 2011. Workload measurement in a communication application operated through a P300-based brain-computer interface. Journal of Neural Engineering 8(2): 025028. doi: 10.1088/1741-2560/8/2/025028 S1741-2560(11)77552-8.CrossRefGoogle Scholar
  87. Rocon, E., J.A. Gallego, L. Barrios, A.R. Victoria, J. Ibanez, D. Farina, F. Negro, J.L. Dideriksen, S. Conforto, T. D’Alessio, G. Severini, J.M. Belda-Lois, L.Z. Popovic, G. Grimaldi, M. Manto, and J.L. Pons. 2010. Multimodal BCI-mediated FES suppression of pathological tremor. In Conference proceedings of the IEEE Engineering in Medicine and Biology Society 2010, 3337–3340. doi: 10.1109/IEMBS.2010.5627914.
  88. Rohm, M., G.R. Müller-Putz, A. von Ascheberg, M. Gubler, M. Tavella, J.d.R. Millán, and R. Rupp. 2011. Modular FES-hybrid orthosis for individualized setup of BCI controlled motor substitution and recovery. International Journal of Bioelectromagnetism 13: 127–128.Google Scholar
  89. Rohm, M., M. Schneiders, C. Müller, A. Kreilinger, V. Kaiser, G.R. Müller-Putz, and R. Rupp. 2013. Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artificial Intelligence in Medicine 59(2): 133–142. doi: 10.1016/j.artmed.2013.07.004.CrossRefGoogle Scholar
  90. Rupp, R., and H.J. Gerner. 2007. Neuroprosthetics of the upper extremity-clinical application in spinal cord injury and challenges for the future. Acta Neurochirurgica Supplement 97(Pt 1): 419–426.Google Scholar
  91. Rupp, R., G.R. Müller-Putz, G. Pfurtscheller, H.J. Gerner, and G. Vossius. 2008. Evaluation of control methods for grasp neuroprostheses based on residual movements.; myoelectrical activity and cortical signals. Biomedical Technology (Berlin) 53(Suppl. 1): 2.Google Scholar
  92. Rupp, R., A. Kreilinger, M. Rohm, V. Kaiser, and G.R. Müller-Putz. 2012. Development of a non-invasive.; multifunctional grasp neuroprosthesis and its evaluation in an individual with a high spinal cord injury. In Conference proceedings: Annual international conference of the IEEE Engineering in Medicine and Biology Society IEEE engineering in Medicine and Biology Society conference 2012, 1835–1838. doi: 10.1109/EMBC.2012.6346308.
  93. Scherer, M.J. 2002. The change in emphasis from people to person: Introduction to the special issue on assistive technology. Disability and Rehabilitation 24(1–3): 1–4.CrossRefGoogle Scholar
  94. Schill, O., R. Wiegand, B. Schmitz, R. Matthies, U. Eck, C. Pylatiuk, M. Reischl, S. Schulz, and R. Rupp. 2011. OrthoJacket: An active FES-hybrid orthosis for the paralysed upper extremity. Biomedical Technology (Berlin) 56(1): 35–44. doi: 10.1515/BMT.2010.056.CrossRefGoogle Scholar
  95. Schöner, G., M. Dose, and C. Engels. 1995. Dynamics of behavior: Theory and applications for autonomous robot architectures. Robotics and Autonomous Systems 16: 213–245.CrossRefGoogle Scholar
  96. Schreuder, M., T. Rost, and M. Tangermann. 2011. Listen, you are writing! speeding up online spelling with a dynamic auditory BCI. Frontiers in Neuroscience 5: 112. doi: 10.3389/fnins.2011.00112.CrossRefGoogle Scholar
  97. Sellers, E.W., and E. Donchin. 2006. A P300-based brain-computer interface: Initial tests by ALS patients. Clinical Neurophysiology 117(3): 538–548. doi:S1388-2457(05)00460-8.CrossRefGoogle Scholar
  98. Shih, J.J., D.J. Krusienski, and J.R. Wolpaw. 2012. Brain-computer interfaces in medicine. Mayo Clinic Proceedings 87(3): 268–279. doi:10.1016/j.mayocp.2011.12.008.CrossRefGoogle Scholar
  99. Silvoni, S., C. Volpato, M. Cavinato, M. Marchetti, K. Priftis, A. Merico, P. Tonin, K. Koutsikos, F. Beverina, and F. Piccione. 2009. P300-based brain-computer interface communication: Evaluation and follow-up in amyotrophic lateral sclerosis. Frontiers in Neuroscience 3: 60. doi: 10.3389/neuro.20.001.2009.Google Scholar
  100. Smith, B., P.H. Peckham, M.W. Keith, and D.D. Roscoe. 1987. An externally powered, multichannel, implantable stimulator for versatile control of paralyzed muscle. IEEE Transactions on Biomedical Engineering 34(7): 499–508.CrossRefGoogle Scholar
  101. Snoek, G.J., H.J. Hermens, D. Maxwell, and F. Biering-Sorensen. 2004. Survey of the needs of patients with spinal cord injury: Impact and priority for improvement in hand function in tetraplegics. Spinal Cord 42(9): 526–532. doi: 10.1038/sj.sc.31016383101638.CrossRefGoogle Scholar
  102. Sutton, S., M. Braren, J. Zubin, and E.R. John. 1965. Evoked-potential correlates of stimulus uncertainty. Science 150(3700): 1187–1188.CrossRefGoogle Scholar
  103. Tavella, M., R. Leeb, R. Rupp, and Millán, J.d.R. (2010) Towards natural non-invasive hand neuroprostheses for daily living. In Conference proceedings of the IEEE Engineering in Medicine and Biology Society 2010, 126–129. doi: 10.1109/IEMBS.2010.5627178.
  104. Tonin, L., R. Leeb, M. Tavella, S. Perdikis, and J.d.R. Millán. 2010. The role of shared-control in BCI-based telepresence. In Proceedings of the 2010 IEEE international conference on systems, man and cybernetics, Istanbul.Google Scholar
  105. Tonin, L., T. Carlson, R. Leeb, and J.d.R. Millán. 2011. Brain-controlled telepresence robot by motor-disabled people. In Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society EMBC 2011, Shanghai.Google Scholar
  106. Truelsen, T., E. Prescott, M. Gronbaek, P. Schnohr, and G. Boysen. 1997. Trends in stroke incidence. The Copenhagen City Heart Study. Stroke 28(10): 1903–1907.CrossRefGoogle Scholar
  107. van den Berg, M.E., J.M. Castellote, I. Mahillo-Fernandez, and J. de Pedro-Cuesta. 2010. Incidence of spinal cord injury worldwide: A systematic review. Neuroepidemiology 34(3), discussion 192. doi:000279335.Google Scholar
  108. van den Honert, C., and J.T. Mortimer. 1979. The response of the myelinated nerve fiber to short duration biphasic stimulating currents. Annals of Biomedical Engineering 7(2): 117–125.CrossRefGoogle Scholar
  109. Vanacker, G., J.d.R. Millán, E. Lew, P.W. Ferrez, F. Galán, J. Philips, H. Van Brussel, and M. Nuttin. 2007. Context-based filtering for assisted brain-actuated wheelchair driving. Computational Intelligence and Neuroscience 2007: ID 25130.Google Scholar
  110. Vanhooydonck, D., E. Demeester, M. Nuttin, and H. Van Brussel. 2003. Shared control for intelligent wheelchairs: An implicit estimation of the user intention. In Proceedings of the 1st international workshop advances in service robot, Tallinn.Google Scholar
  111. Veldink, J.H., J.H. Wokke, G. van der Wal, J.M. Vianney de Jong, and L.H. van den Berg. 2002. Euthanasia and physician-assisted suicide among patients with amyotrophic lateral sclerosis in the Netherlands. New England Journal of Medicine 346(21): 1638–1644. doi: 10.1056/NEJMsa012739346/21/1638.CrossRefGoogle Scholar
  112. Warlow, C., J. van Gijn, M. Dennis, J. Wardlaw, J. Bamford, P. Sandercock, G. Rinkel, P. Langhorne, C. Sudlow, and P. Rothwell. 2008. Stroke: Practical management, 3rd ed. Oxford: Blackwell Publishing.CrossRefGoogle Scholar
  113. Wheeler, C.A., and P.H. Peckham. 2009. Wireless wearable controller for upper-limb neuroprosthesis. Journal of Rehabilitation Research and Development 46(2): 243–256.CrossRefGoogle Scholar
  114. Wolpaw, J.R., N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan. 2002. Brain-computer interfaces for communication and control. Clinical Neurophysiology 113(6): 767–791. doi:S1388245702000573.CrossRefGoogle Scholar
  115. Zickler, C., V.D. Donna, V. Kaiser, A. Al-Khodairy, S. Kleih, A. Kübler, M. Malavasi, D. Mattia, S. Mongardi, C. Neuper, M. Rohm, and R. Rupp. 2009. BCI-applications for people with disabilities: Defining user needs and user requirements. In Assistive technology from adapted equipment to inclusive environments: AAATE, 185–189. Amsterdam: IOS Press.Google Scholar
  116. Zickler, C., A. Riccio, F. Leotta, S. Hillian-Tress, S. Halder, E. Holz, P. Staiger-Salzer, E.J. Hoogerwerf, L. Desideri, D. Mattia, and A. Kübler. 2011. A brain-computer interface as input channel for a standard assistive technology software. Clinical EEG and Neuroscience 42(4): 236–244.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Rüdiger Rupp
    • 1
    Email author
  • Sonja C. Kleih
    • 2
  • Robert Leeb
    • 3
  • José del R. Millan
    • 3
  • Andrea Kübler
    • 2
  • Gernot R. Müller-Putz
    • 4
  1. 1.Spinal Cord Injury CenterUniversity Hospital HeidelbergHeidelbergGermany
  2. 2.Department of PsychologyUniversity of WürzburgWürzburgGermany
  3. 3.Center for NeuroprostheticsÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  4. 4.Institute for Knowledge Discovery, Laboratory of Brain-Computer InterfacesGraz University of TechnologyGrazAustria

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