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The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome

Abstract

The history of brain-computer interfaces (BCI) developed from a mere idea in the days of early digital technology to today’s highly sophisticated approaches for signal detection, recording, and analysis. In the 1960s, electroencephalography (EEG) was tied to the laboratory due to equipment and recording requirements. Today, amplifiers exist that are built in the electrode cap and are so resistant to movement artefacts that data collection in the field is no longer a critical issue. Within 60 years, the field has moved from simple and artefact-sensitive EEG recording to making real the vision of brain-computer communication. In the last 40 years, direct brain-computer interaction went from simple communication programs to sophisticated BCI-controlled applications. In the past two decades, much research was conducted with locked-in individuals, and since the 2010s, independent home use by exemplary patients has been demonstrated. In these patients with locked-in syndrome (LIS), BCI were installed at their home and long-term usage was established, resulting in increased quality of life (QOL). Maintaining communication in disorders leading to LIS contributes significantly to the patients’ sense of being full persons. BCI as an assistive technology will likely be perceived as integral part of the self: insofar as it can prevent total loss of communication and the ensuing social isolation, it enables essential conditions for the subjective and intersubjective experience of personhood.

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Notes

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    The issue of whether the “mental life” in so responding patients (wrongly) diagnosed as being in the vegetative state was the same as in healthy subjects has been tackled in later studies [90].

References

  1. 1.

    Brunner, C., et al., (2015) BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-computer interfaces 2 (1): 1–10.

  2. 2.

    Kübler, A., B. Kotchoubey, J. Kaiser, J.R. Wolpaw, and N. Birbaumer. 2001. Brain-computer communication: Unlocking the locked in. Psychological Bulletin 127 (3): 358–375.

    Google Scholar 

  3. 3.

    Millan, J.D., et al. 2010. Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges. Frontiers in Neuroscience 4.

  4. 4.

    Rupp, R., et al., Brain–Computer Interfaces and Assistive Technology, In Brain-computer-interfaces in their ethical, social, and cultural contexts. G. Grubler and E. Hildt, Editors. 2014, Springer Dordrecht Heidelberg. p. 7–38.

  5. 5.

    Kennedy, P.R., and R.A. Bakay. 1998. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9 (8): 1707–1711.

    Google Scholar 

  6. 6.

    Hochberg, L.R., M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D. Penn, and J.P. Donoghue. 2006. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442 (7099): 164–171.

    Google Scholar 

  7. 7.

    Kennedy, P.R., R.A.E. Bakay, M.M. Moore, K. Adams, and J. Goldwaithe. 2000. Direct control of a computer from the human central nervous system. IEEE Transactions on Rehabilitation Engineering 8 (2): 198–202.

    Google Scholar 

  8. 8.

    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.

    Google Scholar 

  9. 9.

    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. Lancet 381 (9866): 557–564.

    Google Scholar 

  10. 10.

    Downey, J.E., et al. 2017. Motor cortical activity changes during neuroprosthetic-controlled object interaction. Scientific Reports 7 (1): p. 16947.

    Google Scholar 

  11. 11.

    Velliste, M., S.D. Kennedy, A.B. Schwartz, A.S. Whitford, J.W. Sohn, and A.J.C. McMorland. 2014. Motor cortical correlates of arm resting in the context of a reaching task and implications for prosthetic control. The Journal of Neuroscience 34 (17): 6011–6022.

    Google Scholar 

  12. 12.

    Downey, J.E., N. Schwed, S.M. Chase, A.B. Schwartz, and J.L. Collinger. 2018. Intracortical recording stability in human brain-computer interface users. Journal of Neural Engineering 15 (4): 046016.

    Google Scholar 

  13. 13.

    Brunner, P., et al. 2011. Rapid communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG). Frontiers in Neuroscience 5: 5.

    Google Scholar 

  14. 14.

    Vansteensel, M.J., E.G.M. Pels, M.G. Bleichner, M.P. Branco, T. Denison, Z.V. Freudenburg, P. Gosselaar, S. Leinders, T.H. Ottens, M.A. van den Boom, P.C. van Rijen, E.J. Aarnoutse, and N.F. Ramsey. 2016. Fully implanted brain-computer Interface in a locked-in patient with ALS. The New England Journal of Medicine 375 (21): 2060–2066.

    Google Scholar 

  15. 15.

    Botrel, L., E.M. Holz, and A. Kübler. 2015. Brain painting V2: Evaluation of P300-based brain-computer interface for creative expression by an end-user following the user-centered design. Brain-Computer Interfaces 2 (2–3): 135–149.

    Google Scholar 

  16. 16.

    Holz, E.M., et al. 2015. Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: A case study. Archives of Physical Medicine and Rehabilitation 96 (3 Suppl): S16–S26.

    Google Scholar 

  17. 17.

    Holz, E.M., L. Botrel, and A. Kübler. 2015. Independent home use of brain painting improves quality of life of two artists in the locked-in state diagnosed with amyotrophic lateral sclerosis. Brain-Computer Interfaces 2 (2–3): 117–134.

    Google Scholar 

  18. 18.

    Sellers, E.W., T.M. Vaughan, and J.R. Wolpaw. 2010. A brain-computer interface for long-term independent home use. Amyotrophic Lateral Sclerosis 11 (5): 449–455.

    Google Scholar 

  19. 19.

    Berger, H. 1929. Über das Elektrenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten 87 (1): 527–570.

    Google Scholar 

  20. 20.

    Borck, C. 2015. Hirnströme. Eine Kulturgeschichte der Elektroenzephalographie. In Wissenschaftsgeschichte, ed. M. Hagner and H.-J. Rheinberger. Göttingen: Wallstein Verlag.

    Google Scholar 

  21. 21.

    Estrin, T. 1965. On-line electroencephalosraphic digital computing system. Electroencephalography and Clinical Neurophysiology 19 (5): 524–526.

    Google Scholar 

  22. 22.

    Vidal, J.J. 1973. Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering 2: 157–180.

    Google Scholar 

  23. 23.

    Gruzelier, J.H. 2009. A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cognitive Processing 10 (Suppl 1): S101–S109.

    Google Scholar 

  24. 24.

    Skinner, B.F. 1955. The control of human behavior. Transactions of the New York Academy of Sciences 17 (7): 547–551.

    Google Scholar 

  25. 25.

    Kübler, A., B. Kotchoubey, T. Hinterberger, N. Ghanayim, J. Perelmouter, M. Schauer, C. Fritsch, E. Taub, and N. Birbaumer. 1999. The thought translation device: A neurophysiological approach to communication in total motor paralysis. Experimental Brain Research 124 (2): 223–232.

    Google Scholar 

  26. 26.

    Miller, N.E., and L. DiCara. 1967. Instrumental learning of heart rate changes in curarized rats: Shaping, and specificity to discriminative stimulus. Journal of Comparative and Physiological Psychology 63 (1): 12–19.

    Google Scholar 

  27. 27.

    Taub, E. 2010. What psychology as a science owes Neal Miller: The example of his biofeedback research. Biofeedback 38 (3): 108–117.

    Google Scholar 

  28. 28.

    Dworkin, B.R., and N.E. Miller. 1986. Failure to replicate visceral learning in the acute curarized rat preparation. Behavioral Neuroscience 100 (3): 299–314.

    Google Scholar 

  29. 29.

    Edmund, J. 1925. Progressive relaxation. The American Journal of Psychology 36 (1): 73–87.

    Google Scholar 

  30. 30.

    Schultz, J.H., and W. Luthe. 1959. Autogenic training: A psychophysiologic approach to psychotherapy. Oxford: Grune & Stratton.

    Google Scholar 

  31. 31.

    Schwartz, M.S., et al. 2016. The history and definitions of biofeedback and applied psychophysiology. In Biofeedback - a Practitioner's Guide, M.S. Schwartz and F. Andrasik, 3–23. New York: The Guilford Press.

    Google Scholar 

  32. 32.

    Kamiya, J. 1971. Biofeedback training in voluntary control of EEG alpha rhythms. California Medicine 115 (3): 44.

    Google Scholar 

  33. 33.

    Irimia, D.C., R. Ortner, M.S. Poboroniuc, B.E. Ignat, and C. Guger. 2018. High classification accuracy of a motor imagery based brain-computer interface for stroke rehabilitation training. Frontiers in Robotics and AI 5 (130).

  34. 34.

    Blankertz, B., et al. 2006. The Berlin brain-computer interface: Machine learning based detection of user specific brain states. Journal of Universal Computer Science 12 (6): 581–607.

    Google Scholar 

  35. 35.

    Kindermans, P.J., M. Schreuder, B. Schrauwen, K.R. Müller, and M. Tangermann. 2014. True zero-training brain-computer interfacing - an online study. PLoS One 9 (7): e102504.

    Google Scholar 

  36. 36.

    Blankertz, B., G. Curio, and K.R. Müller. 2002. Classifying single trial EEG: Towards brain computer interfacing. In Advances in neural information processing systems, vol. 14, 157–164.

    Google Scholar 

  37. 37.

    Blankertz, B., R. Tomioka, S. Lemm, M. Kawanabe, and K.R. Muller. 2008. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine 25 (1): 41–56.

    Google Scholar 

  38. 38.

    McFarland, D.J., L.M. McCane, S.V. David, and J.R. Wolpaw. 1997. Spatial filter selection for EEG-based communication. Electroencephalography and Clinical Neurophysiology 103 (3): 386–394.

    Google Scholar 

  39. 39.

    Fazli, S., F. Popescu, M. Danóczy, B. Blankertz, K.R. Müller, and C. Grozea. 2009. Subject-independent mental state classification in single trials. Neural Networks 22 (9): 1305–1312.

    Google Scholar 

  40. 40.

    Kübler, A., et al. 2005. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64 (10): 1775–1777.

    Google Scholar 

  41. 41.

    Blankertz, B., G. Dornhege, M. Krauledat, K.R. Müller, and G. Curio. 2007. The non-invasive Berlin brain-computer Interface: Fast acquisition of effective performance in untrained subjects. Neuroimage 37 (2): 539–550.

    Google Scholar 

  42. 42.

    Kübler, A. 2017. Quo vadis P300 BCI? 5th International Winter Conference on Brain-Computer Interface IEEE. High 1 Resort, South Korea.

  43. 43.

    Kübler, A., D. Mattia, R. Rupp, and M. Tangermann. 2013. Facing the challenge: Bringing brain-computer interfaces to end-users. Artificial Intelligence in Medicine 59 (2): 55–60.

    Google Scholar 

  44. 44.

    Lantz, D.L., and M.B. Sterman. 1988. Neuropsychological assessment of subjects with uncontrolled epilepsy: Effects of EEG feedback training. Epilepsia 29 (2): 163–171.

    Google Scholar 

  45. 45.

    Sterman, M. 1977. Effects of sensorimotor EEG feedback training on sleep and clinical manifestations of epilepsy. In Biofeedback and Behavior, ed. L.H. Beatty Jn A., 167–200. New York: Plenum Press.

    Google Scholar 

  46. 46.

    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 398 (6725): 297–298.

    Google Scholar 

  47. 47.

    Halder, S., I. Käthner, and A. Kübler. 2016. Training leads to increased auditory brain-computer interface performance of end-users with motor impairments. Clinical Neurophysiology 127 (2): 1288–1296.

  48. 48.

    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.

    Google Scholar 

  49. 49.

    Holz, E.M., L. Botrel, and A. Kübler. 2014. Independent BCI use in two patients diagnosed with amyotrophic lateral sclerosis. In 6th International BCI Conference, ed. G. Müller-Putz et al. Graz: Technische Universität Graz.

    Google Scholar 

  50. 50.

    Kaufmann, T., S.M. Schulz, A. Köblitz, G. Renner, C. Wessig, and A. Kübler. 2013. Face stimuli effectively prevent brain-computer interface inefficiency in patients with neurodegenerative disease. Clinical Neurophysiology 124 (5): 893–900.

    Google Scholar 

  51. 51.

    Sterman, M.B., R.C. Howe, and L.R. Macdonald. 1970. Facilitation of spindle-burst sleep by conditioning of electroencephalographic activity while awake. Science 167 (3921): 1146–1148.

    Google Scholar 

  52. 52.

    Roth, S.R., M.B. Sterman, and C.D. Clemente. 1967. Comparison of EEG correlates of reinforcement, internal inhibition and sleep. Electroencephalography and Clinical Neurophysiology 23 (6): 509–520.

    Google Scholar 

  53. 53.

    Sterman, M.B. 2000. Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning. Clinical Electroencephalography 31 (1): 45–55.

    Google Scholar 

  54. 54.

    Sterman, M.B. 2010. Biofeedback in the treatment of epilepsy. Cleveland Clinic Journal of Medicine 77 (Suppl 3): S60–S67.

    Google Scholar 

  55. 55.

    Pfurtscheller, G., and C. Neuper. 1997. Motor imagery activates primary sensorimotor area in humans. Neuroscience Letters 239 (2–3): 65–68.

    Google Scholar 

  56. 56.

    Wolpaw, J.R., D.J. McFarland, G.W. Neat, and C.A. Forneris. 1991. An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology 78 (3): 252–259.

    Google Scholar 

  57. 57.

    Pfurtscheller, G., and C. Neuper. 2006. Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. Progress in Brain Research 159: 433–437.

    Google Scholar 

  58. 58.

    Walter, W.G., et al. 1964. Contingent negative variation: An electric sign of sensorimotor association and expectancy in the human brain. Nature 203: 380–384.

    Google Scholar 

  59. 59.

    Cui, R.Q., D. Huter, W. Lang, and L. Deecke. 1999. Neuroimage of voluntary movement: Topography of the Bereitschaftspotential, a 64-channel DC current source density study. Neuroimage 9 (1): 124–134.

    Google Scholar 

  60. 60.

    Kornhuber, H., and L. Deecke. 1965. Hirnpotentialanderungen bei Willkürbewegungen und passiven Bewegungen des Menschen - Bereitschaftspotential und reafferente Potentiale. Pflügers Archiv für die gesamte Physiologie des Menschen und der Tiere 284 (1): 1–17.

  61. 61.

    Birbaumer, N., et al. 1990. Slow potentials of the cerebral cortex and behavior. Physiological Reviews 70 (1): 1–41.

    Google Scholar 

  62. 62.

    Kotchoubey, B., U. Strehl, S. Holzapfel, D. Schneider, V. Blankenhorn, and N. Birbaumer. 1999. Control of cortical excitability in epilepsy. Advances in Neurology 81: 281–290.

    Google Scholar 

  63. 63.

    Strehl, U., et al. 2014. Sustained reduction of seizures in patients with intractable epilepsy after self-regulation training of slow cortical potentials - 10 years after. Frontiers in Human Neuroscience 8: 604.

  64. 64.

    Kübler, A., E.M. Holz, A. Riccio, C. Zickler, T. Kaufmann, S.C. Kleih, P. Staiger-Sälzer, L. Desideri, E.J. Hoogerwerf, and D. Mattia. 2014. The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications. PLoS One 9 (12): e112392.

    Google Scholar 

  65. 65.

    ISO 9241-210, Ergonomics of human system interaction - Part 210: Human-centred design for interactive systems (formerly known as 13407). International Organization for Standardization (ISO). Switzerland, 2008.

  66. 66.

    Lorenz, R., J. Pascual, B. Blankertz, and C. Vidaurre. 2014. Towards a holistic assessment of the user experience with hybrid BCIs. Journal of Neural Engineering 11 (3): 035007.

    Google Scholar 

  67. 67.

    van der Waal, M., M. Severens, J. Geuze, and P. Desain. 2012. Introducing the tactile speller: An ERP-based brain-computer interface for communication. Journal of Neural Engineering 9 (4): 045002.

    Google Scholar 

  68. 68.

    Zickler, C., et al. 2009. BCI applications for people with disabilities: Defining user needs and user requirements. In 10th Association of the Advancement of Assistive Technology in Europe Conference, ed. P.L. Emiliani et al., 185–189. Italy: IOS Press Florence.

    Google Scholar 

  69. 69.

    De Vos, M., K. Gandras, and S. Debener. 2014. Towards a truly mobile auditory brain-computer interface: Exploring the P300 to take away. International Journal of Psychophysiology 91 (1): 46–53.

    Google Scholar 

  70. 70.

    Blum, S., et al. 2017. EEG recording and online signal processing on android: A multiapp framework for brain-computer interfaces on smartphone. BioMed Research International 2017: 3072870.

    Google Scholar 

  71. 71.

    Bleichner, M.G., and S. Debener. 2017. Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG. Frontiers in Human Neuroscience 11: 163.

  72. 72.

    Bruno, M.A., S. Laureys, and A. Demertzi. 2013. Coma and disorders of consciousness. Handbook of Clinical Neurology 118: 205–213.

    Google Scholar 

  73. 73.

    Smith, E., and M. Delargy. 2005. Locked-in syndrome. British Medical Journal 330 (7488): 406–409.

  74. 74.

    Tavalaro, J., and R. Tayson. 1998. Look up for yes. Penguin Publishing Group.

  75. 75.

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

    Google Scholar 

  76. 76.

    Kuebler, A., B. Kotchoubey, H.P. Salzmann, N. Ghanayim, J. Perelmouter, V. Hömberg, and N. Birbaumer. 1998. Self-regulation of slow cortical potentials in completely paralyzed human patients. Neuroscience Letters 252 (3): 171–174.

    Google Scholar 

  77. 77.

    Bach, J.R. 1993. Amyotrophic lateral sclerosis. Communication status and survival with ventilatory support. American Journal of Physical Medicine & Rehabilitation 72 (6): 343–349.

    Google Scholar 

  78. 78.

    Rousseau, M.C., et al. 2015. Quality of life in patients with locked-in syndrome: Evolution over a 6-year period. Orphanet Journal of Rare Diseases 10: 88.

    Google Scholar 

  79. 79.

    Burchardi, N., O. Rauprich, M. Hecht, M. Beck, and J. Vollmann. 2005. Discussing living wills. A qualitative study of a German sample of neurologists and ALS patients. Journal of the Neurological Sciences 237 (1–2): 67–74.

    Google Scholar 

  80. 80.

    Caron, J., and J. Light. 2015. "my world has expanded even though I'm stuck at home": Experiences of individuals with amyotrophic lateral sclerosis who use augmentative and alternative communication and social media. American Journal of Speech-Language Pathology 24 (4): 680–695.

    Google Scholar 

  81. 81.

    Londral, A., A. Pinto, S. Pinto, L. Azevedo, and M. de Carvalho. 2015. Quality of life in amyotrophic lateral sclerosis patients and caregivers: Impact of assistive communication from early stages. Muscle & Nerve 52 (6): 933–941.

    Google Scholar 

  82. 82.

    Korner, S., et al. 2013. Speech therapy and communication device: Impact on quality of life and mood in patients with amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 14 (1): 20–25.

  83. 83.

    Snoeys, L., G. Vanhoof, and E. Manders. 2013. Living with locked-in syndrome: An explorative study on health care situation, communication and quality of life. Disability and Rehabilitation 35 (9): 713–718.

    Google Scholar 

  84. 84.

    Herweg, A., J. Gutzeit, S. Kleih, and A. Kübler. 2016. Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biological Psychology (121(Pt A): 117–124.

  85. 85.

    Hösle, A. 2014. Between neuro-potentials and aesthetic perception. Pingo ergo sum. In Brain-Computer Interfaces in their ethical, social and cultural contexts, ed. G. Grübler and E. Hildt, 99–108. Dordrecht: Springer Netherlands.

    Google Scholar 

  86. 86.

    Zickler, C., S. Halder, S.C. Kleih, C. Herbert, and A. Kübler. 2013. Brain painting: Usability testing according to the user-centered design in end users with severe motor paralysis. Artificial Intelligence in Medicine 59 (2): 99–110.

    Google Scholar 

  87. 87.

    Hill, N.J., T.N. Lal, M. Schröder, T. Hinterberger, B. Wilhelm, F. Nijboer, U. Mochty, G. Widman, C. Elger, B. Schölkopf, A. Kübler, and N. Birbaumer. 2006. Classifying EEG and ECoG signals without subject training for fast BCI implementation: Comparison of nonparalyzed and completely paralyzed subjects. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14: 183–186.

  88. 88.

    Wettersten, J.R. Karl Popper: Critical rationalism. [Internet Encyclopedia of Philosophy ] [cited 2019 23.03.2019]; Available from: https://www.iep.utm.edu/cr-ratio/#H8. Accessed 14 Apr 2019.

  89. 89.

    Owen, A.M., M.R. Coleman, M. Boly, M.H. Davis, S. Laureys, and J.D. Pickard. 2006. Detecting awareness in the vegetative state. Science 313 (5792): 1402.

    Google Scholar 

  90. 90.

    Naci, L., L. Sinai, and A.M. Owen. 2017. Detecting and interpreting conscious experiences in behaviorally non-responsive patients. Neuroimage 145 (Pt B): 304–313.

  91. 91.

    Chaudhary, U., et al. 2017. Brain-computer interface-based communication in the completely locked-in state. PLoS Biology 15 (1): e1002593.

    Google Scholar 

  92. 92.

    Guger, C., R. Spataro, B.Z. Allison, A. Heilinger, R. Ortner, W. Cho, and V. la Bella. 2017. Complete locked-in and locked-in patients: Command following assessment and communication with Vibro-tactile P300 and motor imagery brain-computer Interface tools. Frontiers in Neuroscience 11 (251).

  93. 93.

    Spüler, M. 2019. Questioning the evidence for BCI-based communication in the complete locked-in state. PLoS Biology 17 (4): e2004750.

    Google Scholar 

  94. 94.

    Monti, M.M., et al. 2010. Willful modulation of brain activity in disorders of consciousness. The New England Journal of Medicine 362 (7): 579–589.

    Google Scholar 

  95. 95.

    Giacino, J.T., et al. 2002. The minimally conscious state: Definition and diagnostic criteria. Neurology 58 (3): 349–353.

    Google Scholar 

  96. 96.

    Grosse-Wentrup, M., The elusive goal of BCI-based communication with CLIS-ALS patients, in The 7th International Winter Conference on Brain-Computer Interface IEEE. 2019: High 1 Resort, Korea.

  97. 97.

    Grubler, G., and E. Hildt. 2014. On human-computer interaction in brain-computer interfaces. In Brain-computer-interfaces in their ethical, social, and cultural contexts, ed. G. Grubler and E. Hildt, Springer Dordrecht Heidelberg. p. 183–191.

  98. 98.

    Vidal, F. 2018. Phenomenology of the locked-in syndrome: An overview and some suggestions. Neuroethics. https://doi.org/10.1007/s12152-018-9388-1.

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Kübler, A. The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome . Neuroethics 13, 163–180 (2020). https://doi.org/10.1007/s12152-019-09409-4

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Keywords

  • Amyotrophic lateral sclerosis (ALS), brain-computer interface (BCI), locked-in syndrome (LIS)
  • Communication
  • Quality of life (QOL)
  • User-Centred design