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Brain–computer interfaces for communication and rehabilitation

A Corrigendum to this article was published on 17 February 2017

This article has been updated

Key Points

  • Brain–computer interfaces (BCIs) are starting to prove their efficacy as assistive and rehabilitative technologies in patients with severe motor impairments

  • BCIs can be invasive or noninvasive, and designed to detect and decode a variety of brain signals

  • Assistive BCIs are intended to enable paralyzed patients to communicate or control external robotic devices; rehabilitative BCIs are intended to facilitate neural recovery

  • EEG-based BCIs have enabled some paralyzed patients to communicate, but near-infrared spectroscopy combined with a classical conditioning paradigm is the only successful approach for complete locked-in syndrome

  • The combination of EEG-based BCIs with behavioural physiotherapy is a feasible option for rehabilitation in stroke; the approach is to induce neuroplasticity and restore lost function after stroke

  • There is an urgent need for more large randomized controlled clinical trials using invasive and noninvasive BCIs with long-term follow-ups in patients rather than healthy populations


Brain–computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

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Figure 1: General framework of brain–computer interface (BCI) systems.
Figure 2: Use of a brain–computer interface in severe chronic stroke.

Change history

  • 17 February 2017

    In the initial version of this article, details of the BrainGate2 study were omitted from Table 1. This error has been corrected in the HTML and PDF versions of the article.


  1. 1

    Wyrwicka, W. & Sterman, M. B. Instrumental conditioning of sensorimotor cortex EEG spindles in the waking cat. Physiol. Behav. 3, 703–707 (1968).

    Article  Google Scholar 

  2. 2

    Kamiya, J. in Altered states of consciousness. (ed Tart, C.) 519–529 (New York: Wiley, 1969).

    Google Scholar 

  3. 3

    Fetz, E. E. & Baker, M. A. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J. Neurophysiol. 36, 179–204 (1973).

    Article  CAS  PubMed  Google Scholar 

  4. 4

    Vidal, J.-J. Toward direct brain-computer communication. Annu. Rev. Biophys. Bioeng. 2, 157–180 (1973). The first paper describing a brain computer interface and the hypothetical learning mechanisms involved.

    Article  CAS  PubMed  Google Scholar 

  5. 5

    Sterman, M. B., Wyrwicka, W. & Roth, S. Electrophysiological correlates and neural substrates of alimentary behavior in the cat. Ann. NY Acad. Sci. 157, 723–739 (1969).

    Article  CAS  PubMed  Google Scholar 

  6. 6

    Sterman, M. & Friar, L. Suppression of seizures in epileptic Following on sensorimotor EEG feedback training. Electroencephalogr. Clin. Neurophysiol. 33, 89–95 (1972).

    Article  CAS  PubMed  Google Scholar 

  7. 7

    Lubar, J. F. & Shouse, M. N. EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR) - A preliminary report. Biofeedback Self Regul. 1, 293–306 (1976).

    Article  CAS  PubMed  Google Scholar 

  8. 8

    Sterman, M. B. & Macdonald, L. R. Effects of central cortical EEG feedback training on incidence of poorly controlled seizures. Epilepsia 19, 207–222 (1978).

    Article  CAS  PubMed  Google Scholar 

  9. 9

    Chapin, J. K., Moxon, K. A., Markowitz, R. S. & Nicolelis, M. A. L. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2, 664–670 (1999).

    Article  CAS  PubMed  Google Scholar 

  10. 10

    Donoghue, J. P. Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5, 1085–1088 (2002).

    Article  CAS  PubMed  Google Scholar 

  11. 11

    Nicolelis, M. A. L. Actions from thoughts. Nature 409, 403–407 (2001).

    Article  CAS  PubMed  Google Scholar 

  12. 12

    Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. 13

    Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct Cortical Control of 3D Neuroprosthetic Devices. Sci. 296, 1829–1832 (2002).

    Article  CAS  Google Scholar 

  14. 14

    Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. & Shenoy, K. V. A high-performance brain-computer interface. Nature 442, 195–198 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. 15

    Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).

    Article  CAS  PubMed  Google Scholar 

  16. 16

    Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. Brain-machine interface: Instant neural control of a movement signal. Nature 416, 141–142 (2002).

    Article  CAS  PubMed  Google Scholar 

  17. 17

    Carmena, J. M. et al. Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol. 1, e2 (2003). This paper provides the most advanced and detailed neurophysiological analysis of the neuronal mechanisms behind brain–computer interface control of complex movements.

    Article  CAS  Google Scholar 

  18. 18

    Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. 19

    Donoghue, J. P., Nurmikko, A., Black, M. & Hochberg, L. R. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J. Physiol. 579, 603–611 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Birbaumer, N., Ramos Murguialday, A., Weber, C. & Montoya, P. Chapter 8 neurofeedback and brain-computer Interface: clinical applications. Int. Rev. Neurobiol. 86, 107–117 (2009).

    Article  PubMed  Google Scholar 

  21. 21

    Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H. & Kaiser, J. Neurofeedback treatment for attention-deficit / hyperactivity disorder in children: a comparison with methylphenidate. Appl. Psychophysiol. Biofeedback 28, 1–12 (2003).

    Article  PubMed  Google Scholar 

  22. 22

    Monastra, V. J. et al. Electroencephalographic biofeedback in the treatment of attention-deficit / hyperactivity disorder. J. Neurother. 9, 5–34 (2006).

    Article  Google Scholar 

  23. 23

    Kotchoubey, B. et al. Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 42, 406–416 (2001).

    Article  CAS  PubMed  Google Scholar 

  24. 24

    Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012). The first paper describing multidemensional movement control of an arm–hand robotic device using an implanted microelectrode array in the primary motor cortex of a paralyzed patient.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Jarosiewicz, B. et al. Virtual typing by people with tetraplegia using a stabilized, self-calibrating intracortical brain-computer interface. IEEE BRAIN Gd. Challenges Conf. Washington, DC 7, 1–11 (2014).

    Google Scholar 

  27. 27

    Pfurtscheller, G., Müller, G. R., Pfurtscheller, J., Gerner, H. J. & Rupp, R. 'Thought ' – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 33–36 (2003).

    Article  CAS  PubMed  Google Scholar 

  28. 28

    Caria, A., Sitaram, R. & Birbaumer, N. Real-time fMRI: a tool for local brain regulation. Neuroscientist. 18, 487–501 (2012).

    Article  PubMed  Google Scholar 

  29. 29

    Chaudhary, U., Birbaumer, N. & Curado, M. R. Brain-machine interface (BMI) in paralysis. Ann. Phys. Rehabil. Med. 58, 9–13 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. 30

    Nijboer, F. et al. An auditory brain–computer interface (BCI). J. Neurosci. Methods 167, 43–50 (2008).

    Article  PubMed  Google Scholar 

  31. 31

    Chatterjee, A., Aggarwal, V., Ramos, A., Acharya, S. & Thakor, N. V. A brain-computer interface with vibrotactile biofeedback for haptic information. J. Neuroeng. Rehabil. 4, 1–12 (2007).

    Article  Google Scholar 

  32. 32

    Lugo, Z. R. et al. A vibrotactile p300-based brain-computer interface for consciousness detection and communication. Clin. EEG Neurosci. 45, 14–21 (2014).

    Article  PubMed  Google Scholar 

  33. 33

    Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E. & Donoghue, J. P. Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. J. Neurophysiol. 107, 1337–1355 (2012).

    Article  PubMed  Google Scholar 

  34. 34

    Flint, R. D., Wright, Z. A., Scheid, M. R. & Slutzky, M. W. Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J. Neural Eng. 10, 056005 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35

    So, K., Dangi, S., Orsborn, A. L., Gastpar, M. C. & Carmena, J. M. Subject-specific modulation of local field potential spectral power during brain-machine interface control in primates. J. Neural Eng. 11, 026002 (2014).

    Article  PubMed  Google Scholar 

  36. 36

    Mehring, C. et al. Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex. J. Physiol. Paris 98, 498–506 (2004).

    Article  PubMed  Google Scholar 

  37. 37

    Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

    Article  CAS  PubMed  Google Scholar 

  38. 38

    Georgopoulos, A. P. & Kettner, R. E. & Schwartz, A. B. Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J. Neurosci. 8, 2928–2937 (1988).

    Article  CAS  PubMed  Google Scholar 

  39. 39

    Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M. R. & Donoghue, J. P. Brain-machine interface: Instant neural control of a movement signal. Nature 416, 121–142 (2002).

    Article  Google Scholar 

  40. 40

    Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G. & Moran, D. W. A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63–71 (2004).

    Article  PubMed  Google Scholar 

  41. 41

    Felton, E. a, Wilson, J. A., Williams, J. C. & Garell, P. C. Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. J. Neurosurg. 106, 495–500 (2007).

    Article  PubMed  Google Scholar 

  42. 42

    Clancy, K. B., Koralek, A. C., Costa, R. M., Feldman, D. E. & Carmena, J. M. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 17, 807–809 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Birbaumer, N., Elbert, T., Canavan, A. & Rockstroh, B. Slow potentials of the cerebral cortex and behavior. Physiol. Rev. 70, 1–41 (1990).

    Article  CAS  PubMed  Google Scholar 

  44. 44

    Kubler, A. et al. Brain-computer communication: self regulation of slow cortical potentials for verbal communication. Arch. Phys. Med. Rehabil. 82, 1533–1539 (2001).

    Article  CAS  PubMed  Google Scholar 

  45. 45

    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, 120–123 (2003).

    Article  PubMed  Google Scholar 

  46. 46

    Pfurtscheller, G. & Aranibar, A. Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr. Clin. Neurophysiol. 46, 138–146 (1979).

    Article  CAS  PubMed  Google Scholar 

  47. 47

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

    Article  PubMed  Google Scholar 

  48. 48

    Wolpaw, J. R. et al. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002).

    Article  PubMed  Google Scholar 

  49. 49

    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).

    Article  CAS  PubMed  Google Scholar 

  50. 50

    Kübler, A. et al. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Ann. NY Acad. Sci. 1157, 90–100 (2009).

    Article  PubMed  Google Scholar 

  51. 51

    Halder, S. et al. An auditory oddball brain-computer interface for binary choices. Clin. Neurophysiol. 121, 516–523 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. 52

    Pires, G., Nunes, U. & Castelo-Branco, M. Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. J. Neurosci. Methods 195, 270–281 (2011).

    Article  PubMed  Google Scholar 

  53. 53

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

    Article  PubMed  Google Scholar 

  54. 54

    Sellers, E. W., Vaughan, T. M. & Wolpaw, J. R. A brain-computer interface for long-term independent home use. Amyotroph. Lateral Scler. 11, 449–455 (2010).

    Article  PubMed  Google Scholar 

  55. 55

    Lesenfants, D. et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J. Neural Eng. Neural Eng. 11, 035002 (2014).

    Article  CAS  Google Scholar 

  56. 56

    Zhu, D., Bieger, J., Molina, G. G. & Aarts, R. M. A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. (2010).

  57. 57

    Chavarriaga, R. & Millán, J. del R. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 381–388 (2010).

    Article  PubMed  Google Scholar 

  58. 58

    Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  CAS  Google Scholar 

  59. 59

    Birbaumer, N., Ruiz, S. & Sitaram, R. Learned regulation of brain metabolism. Trends Cogn. Sci. 17, 295–302 (2013). An extensive review of basic and clinical neurofeedback studies using learning of metabolic brain resonses (BOLD or oxygenation) and the effects on behaviour and cognition.

    Article  PubMed  Google Scholar 

  60. 60

    DeCharms, R. C. et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21, 436–443 (2004).

    Article  PubMed  Google Scholar 

  61. 61

    Rota, G., Handjaras, G., Sitaram, R., Birbaumer, N. & Dogil, G. Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing. Brain Lang. 117, 123–132 (2011).

    Article  PubMed  Google Scholar 

  62. 62

    Weiskopf, N. et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19, 577–586 (2003).

    Article  PubMed  Google Scholar 

  63. 63

    Yoo, S. S. et al. Brain computer interface using fMRI: spatial navigation by thoughts. Neuroreport 15, 1591–1595 (2004).

    Article  PubMed  Google Scholar 

  64. 64

    Birbaumer, N. et al. Deficient fear conditioning in psychopathy: a functional magnetic resonance imaging study. Arch. Gen. Psychiatry 62, 799–805 (2005).

    Article  PubMed  Google Scholar 

  65. 65

    Linden, D. E. J. et al. Real-time self-regulation of emotion networks in patients with depression. PLoS One (2012).

  66. 66

    Li, X. et al. Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: A preliminary real-time fMRI study. Addict. Biol. 18, 739–748 (2013).

    Article  PubMed  Google Scholar 

  67. 67

    Chaudhary, U., Hall, M., DeCerce, J., Rey, G. & Godavarty, A. Frontal activation and connectivity using near-infrared spectroscopy: verbal fluency language study. Brain Res. Bull. 84, 197–205 (2011).

    Article  PubMed  Google Scholar 

  68. 68

    Chaudhary, U. et al. Motor response investigation in individuals with cerebral palsy using near infrared spectroscopy: pilot study. Appl. Opt. 53, 503–510 (2014).

    Article  CAS  PubMed  Google Scholar 

  69. 69

    Obrig, H. NIRS in clinical neurology - a 'promising' tool? Neuroimage 85, 535–546 (2014).

    Article  PubMed  Google Scholar 

  70. 70

    Gallegos-Ayala, G. et al. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology 82, 1930–1932 (2014). The first report of a controlled case study with BCI in a completely paralyzed, locked-in patient restoring communication.

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71

    Naito, M. et al. A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Trans. Inf. Syst. E90D, 1028–1037 (2007).

    Article  Google Scholar 

  72. 72

    Birbaumer, N. et al. A spelling device for the paralysed. Nature 398, 297–298 (1999).

    Article  CAS  PubMed  Google Scholar 

  73. 73

    Ramos-Murguialday, A. et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108 (2014).

    Article  Google Scholar 

  74. 74

    Birbaumer, N., Murguialday, A. R. & Cohen, L. Brain-computer interface in paralysis. Curr. Opin. Neurol. 21, 634–638 (2008).

    Article  PubMed  Google Scholar 

  75. 75

    Chou, S. M. & Norris, F. H. Amyotrophic lateral sclerosis: Lower motor neuron disease spreading to upper motor neurons. Muscle Nerve 16, 864–869 (1993).

    Article  CAS  PubMed  Google Scholar 

  76. 76

    Bauer, G., Gerstenbrand, F. & Rumpl, E. Varieties of the Locked-in Syndrome. J. Neurol. 221, 77–91 (1979).

    Article  CAS  PubMed  Google Scholar 

  77. 77

    Beukelman, D., Fager, S. & Nordness, A. Communication support for people with ALS. Neurol. Res. Int. 2011, 714693 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  78. 78

    Beukelman, D. & Mirenda, P. Augmentative & alternative communication: Supporting children & adults with complex communication needs. (Paul, H. Brookes, Baltimore, MD, 2005).

    Google Scholar 

  79. 79

    Birbaumer, N. & Cohen, L. G. Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621–636 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

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

    Article  CAS  PubMed  Google Scholar 

  81. 81

    Kennedy, P. R., Bakay, R. A., Moore, M. M., Adams, K. & Goldwaithe, J. Direct control of a computer from the human central nervous system. IEEE Trans. Rehabil. Eng. 8, 198–202 (2000).

    Article  CAS  PubMed  Google Scholar 

  82. 82

    Kennedy, P. et al. Using human extra-cortical local field potentials to control a switch. J. Neural Eng. 1, 72–77 (2004).

    Article  PubMed  Google Scholar 

  83. 83

    Wilhelm, B., Jordan, M. & Birbaumer, N. Communication in locked-in syndrome: effects of imagery on salivary pH. Neurology 67, 534–535 (2006).

    Article  CAS  PubMed  Google Scholar 

  84. 84

    Murguialday, A. R. et al. Transition from the locked in to the completely locked-in state: a physiological analysis. Clin. Neurophysiol. 122, 925–933 (2011).

    Article  PubMed  Google Scholar 

  85. 85

    Birbaumer, N. Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 43, 517–532 (2006).

    Article  PubMed  Google Scholar 

  86. 86

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

    Article  PubMed  PubMed Central  Google Scholar 

  87. 87

    Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl Acad. Sci. USA 101, 17849–17854 (2004).

    Article  CAS  PubMed  Google Scholar 

  88. 88

    Bai, O., Lin, P., Huang, D., Fei, D. Y. & Floeter, M. K. Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients. Clin. Neurophysiol. 121, 1293–1303 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  89. 89

    Thorns, J. et al. Movement initiation and inhibition are impaired in amyotrophic lateral sclerosis. Exp. Neurol. 224, 389–394 (2010).

    Article  PubMed  Google Scholar 

  90. 90

    Birbaumer, N., Piccione, F., Silvoni, S. & Wildgruber, M. Ideomotor silence: the case of complete paralysis and brain-computer interfaces (BCI). Psychol. Res. 76, 183–191 (2012).

    Article  PubMed  Google Scholar 

  91. 91

    Hinterberger, T. et al. Neuronal mechanisms underlying control of a brain – computer interface. Eur. J. Neurosci. 21, 3169–3181 (2005).

    Article  PubMed  Google Scholar 

  92. 92

    Hinterberger, T. et al. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy Behav. 13, 300–306 (2008).

    Article  PubMed  Google Scholar 

  93. 93

    Koralek, A. C. et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483, 331–335 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 94

    Dworkin, B. R. & Miller, N. E. Failure to replicate visceral learning in the acute curarized rat preparation. Behav. Neurosci. 100, 299–314 (1986). This paper describes the failure to establish instrumental learning of physiological responses in the curarized rat and possible reasons for this problem.

    Article  CAS  PubMed  Google Scholar 

  95. 95

    Stocco, A., Lebiere, C. & Anderson, J. R. Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination. Psychol. Rev. 117, 541–574 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  96. 96

    Birbaumer, N. & Chaudhary, U. Learning from brain control: clinical application of brain–computer interfaces. e-Neuroforum 6, 87–95 (2015).

    Article  Google Scholar 

  97. 97

    Furdea, A. et al. A new (semantic) reflexive brain-computer interface: in search for a suitable classifier. J. Neurosci. Methods 203, 233–240 (2012).

    Article  CAS  PubMed  Google Scholar 

  98. 98

    Ruf, C. A., De Massari, D., Wagner-Podmaniczky, F., Matuz, T. & Birbaumer, N. Semantic conditioning of salivary pH for communication. Artif. Intell. Med. 59, 1–8 (2013).

    Article  Google Scholar 

  99. 99

    De Massari, D. et al. Brain communication in the locked-in state. Brain 136, 1989–2000 (2013).

    Article  PubMed  Google Scholar 

  100. 100

    Lulé, D. et al. Brain responses to emotional stimuli in patients with amyotrophic lateral sclerosis (ALS). J. Neurol. 254, 519–527 (2007).

    Article  PubMed  Google Scholar 

  101. 101

    Lulé, D. et al. Life can be worth living in locked-in syndrome. Prog. Brain Res. 177, 339–351 (2009).

    Article  PubMed  Google Scholar 

  102. 102

    Lulé, D. et al. Quality of life in fatal disease: the flawed judgement of the social environment. J. Neurol. 260, 2836–2843 (2013).

    Article  PubMed  Google Scholar 

  103. 103

    Chaudhary, U. & Birbaumer, N. Communication in locked-in state after brainstem stroke: a brain- computer-interface approach. Ann. Transl. Med. 3, 2–4 (2015).

    Google Scholar 

  104. 104

    Simeral, J. D., Kim, S. P., Black, M. J., Donoghue, J. P. & Hochberg, L. R. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J. Neural Eng. 8, 025027 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. 105

    Kübler, A. et al. Self-regulation of slow cortical potentials in completely paralyzed human patients. Neurosci. Lett. 252, 171–174 (1998).

    Article  Google Scholar 

  106. 106

    Piccione, F. et al. P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin. Neurophysiol. 117, 531–537 (2006).

    Article  CAS  PubMed  Google Scholar 

  107. 107

    Sellers, E. W., Ryan, D. B. & Hauser, C. K. Noninvasive brain-computer interface enables communication after brainstem stroke. Sci. Transl. Med. 6, 257re7 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  108. 108

    Cirstea, M. C., Ptito, A. & Levin, M. F. Arm reaching improvements with short-term practice depend on the severity of the motor deficit in stroke. Exp. Brain Res. 152, 476–488 (2003).

    Article  CAS  PubMed  Google Scholar 

  109. 109

    Young, J. & Forster, A. Review of stroke rehabilitation. BMJ 334, 86–90 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  110. 110

    Saka, O., McGuire, A. & Wolfe, C. Cost of stroke in the United Kingdom. Age Ageing 38, 27–32 (2008).

    Article  Google Scholar 

  111. 111

    Langhorne, P., Bernhardt, J. & Kwakkel, G. Stroke rehabilitation. Lancet 377, 1693–1702 (2015).

    Article  Google Scholar 

  112. 112

    Hendricks, H. T., van Limbeek, J., Geurts, A. C. & Zwarts, M. J. Motor recovery after stroke: a systematic review of the literature. Arch. Phys. Med. Rehabil. 83, 1629–1637 (2002).

    Article  PubMed  Google Scholar 

  113. 113

    Ward, N. S. & Cohen, L. G. Mechanisms underlying recovery of motor function after stroke. Arch. Neurol. 61, 1844–1848 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  114. 114

    Taub, E., Uswatte, G. & Pidikiti, R. Constraint-induced movement therapy: a new family of techniques with broad application to physical rehabilitation – a clinical review. J. Rehabil. Res. Dev. 36, 237–251 (1999).

    CAS  PubMed  Google Scholar 

  115. 115

    Wolf, S. L. et al. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA 296, 2095–2104 (2006).

    Article  CAS  PubMed  Google Scholar 

  116. 116

    Buch, E. R. et al. Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 135, 596–614 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  117. 117

    Belda-Lois, J.-M. et al. Rehabilitation of gait after stroke: a review towards a top-down approach. J. Neuroeng. Rehabil. 8, 66 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  118. 118

    Chollet, F. et al. Fluoxetine for motor recovery after acute ischaemic stroke (FLAME): a randomised placebo-controlled trial. Lancet Neurol. 10, 123–130 (2011).

    Article  CAS  PubMed  Google Scholar 

  119. 119

    Savitz, S. I. et al. Stem cells as an emerging paradigm in stroke 3: enhancing the development of clinical trials. Stroke 45, 634–639 (2014).

    Article  PubMed  Google Scholar 

  120. 120

    Ganguly, K., Dimitrov, D. F., Wallis, J. D. & Carmena, J. M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14, 662–667 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. 121

    Gulati, T. et al. Robust neuroprosthetic control from the stroke perilesional cortex. J. Neurosci. 35, 8653–8661 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. 122

    Nishimura, Y., Perlmutter, S. I., Eaton, R. W. & Fetz, E. E. Spike-timing-dependent plasticity in primate corticospinal connections induced during free behavior. Neuron 80, 1301–1309 (2013). This paper describes the neurophysiological bases of BCI applications in spinal cord injury.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. 123

    Lucas, T. H. & Fetz, E. E. Myo-cortical crossed feedback reorganizes primate motor cortex output. J. Neurosci. 33, 5261–5274 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. 124

    Ang, K. K. et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front. Neuroeng. (2014).

  125. 125

    Ono, T. et al. Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front. Neuroeng. (2014).

  126. 126

    Pichiorri, F. et al. Brain–computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 77, 851–865 (2015).

    Article  PubMed  Google Scholar 

  127. 127

    Kasahara, K., DaSalla, C. S., Honda, M. & Hanakawa, T. Neuroanatomical correlates of brain–computer interface performance. Neuroimage 110, 95–100 (2015).

    Article  PubMed  Google Scholar 

  128. 128

    Bensmaia, S. J. & Miller, L. E. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat. Rev. Neurosci. 15, 313–325 (2014).

    Article  CAS  PubMed  Google Scholar 

  129. 129

    Ren, X. et al. Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications. Biomed. Eng. IEEE Trans. 61, 288–296 (2014).

    Article  Google Scholar 

  130. 130

    Jiang, N., Gizzi, L., Mrachacz-Kersting, N., Dremstrup, K. & Farina, D. A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials. Clin. Neurophysiol. 126, 154–159 (2015).

    Article  PubMed  Google Scholar 

  131. 131

    Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  132. 132

    Ouzký, M. Towards concerted efforts for treating and curing spinal cord injury (Council of Europe Parliamentary Assembly document 9401). (2002)

  133. 133

    Van Den Berg, M. E., Castellote, J. M., Mahillo-Fernandez, I. & De Pedro-Cuesta, J. Incidence of spinal cord injury worldwide: a systematic review. Neuroepidemiology 34, 184–192 (2010).

    Article  CAS  PubMed  Google Scholar 

  134. 134

    Wolpaw, J. R. The complex structure of a simple memory. Trends Neurosci. 20, 588–594 (1997).

    Article  CAS  PubMed  Google Scholar 

  135. 135

    Wang, W. et al. An electrocorticographic brain interface in an individual with tetraplegia. PLoS ONE (2013).

  136. 136

    Pfurtscheller, G., Müller, G. R., Pfurtscheller, J. & Gerner, H. J. & Rupp, R. 'Thought' - Control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 33–36 (2003).

    Article  CAS  PubMed  Google Scholar 

  137. 137

    Nguyen, J. S., Su, S. W. & Nguyen, H. T. Experimental study on a smart wheelchair system using a combination of stereoscopic and spherical vision. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013, 4597–4600 (2013).

    PubMed  Google Scholar 

  138. 138

    Kasashima-Shindo, Y. et al. Brain–computer interface training combined with transcranial direct current stimulation in patients with chronic severe hemiparesis: proof of concept study. J. Rehabil. Med. 47, 318–324 (2015).

    Article  PubMed  Google Scholar 

  139. 139

    Enzinger, C. et al. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp. Brain Res. 190, 215–223 (2008).

    Article  PubMed  Google Scholar 

  140. 140

    King, C. E. et al. The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia. J. Neuroeng. Rehabil. 12, 80 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  141. 141

    Pfurtscheller, G., Guger, C., Müller, G., Krausz, G. & Neuper, C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211–214 (2000). The first paper demonstrating noninvasive brain control using a sensorimotor rhythm brain–computer interface in a high spinal cord patient.

    Article  CAS  PubMed  Google Scholar 

  142. 142

    Courtine, G. & Bloch, J. Defining Ecological Strategies in Neuroprosthetics. Neuron 86, 29–33 (2015).

    Article  CAS  PubMed  Google Scholar 

  143. 143

    van den Brand, R. et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 336, 1182–1185 (2012).

    Article  CAS  PubMed  Google Scholar 

  144. 144

    Combaz, A. et al. A comparison of two spelling brain-computer interfaces based on visual P3 and SSVEP in locked-in syndrome. PLoS ONE (2013).

  145. 145

    Bardin, J. C. et al. Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain 134, 769–782 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  146. 146

    Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362, 579–589 (2010).

    Article  CAS  PubMed  Google Scholar 

  147. 147

    Schnakers, C. et al. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. Neurocase 15, 271–277 (2009).

    Article  PubMed  Google Scholar 

  148. 148

    Lulé, D. et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin. Neurophysiol. 124, 101–106 (2013).

    Article  PubMed  Google Scholar 

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The authors are funded by Deutsche Forschungsgemeinschaft (DFG, Bi195, Kosellek), Stiftung Volkswagenwerk (VW), German Ministry of Education and Research (BMBF, grant number MOTOR-BIC (FKZ 136W0053), Ministry of Science, Research and the Arts of Baden Wüttemberg (Az: 32–729.63-0/5-5), Baden-Württemberg Stiftung (ROB-1), EMOIO from the Federal Ministry of Education and Research (BMBF, 524-4013-16SV7196) and Eva and Horst Köhler-Stiftung, (Berlin), EU (Horizon 2020) grant “Brain Train” and “Luminous”, Brain Products, Munich, Germany, and the Wyss Foundation, Geneva, Switzerland.

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All authors contributed equally to all aspects of preparing the manuscript.

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Correspondence to Ujwal Chaudhary or Ander Ramos-Murguialday.

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The authors declare no competing financial interests.

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Alpha waves

Neural oscillations in the frequency range of 8–13 Hz, indicating widespread inhibitory activity in neuronal tissue.

Instrumental learning

A type of learning in which the strength of a behaviour or a physiological response is modified by its consequences (reward or punishment).

Local field potentials

Graded neuroelectrical changes in voltage, generated by the summed synaptic currents flowing from multiple nearby neurons within a small volume of nervous tissue, recorded from inserted microelectrodes.

Single-unit acitivity

Action potentials of single neurons, recorded using inserted microelectrodes

Multi-unit activity

Action potentials of multiple neurons,recorded using an array of multiple microelectrodes.

Cortical preparation

Cortical preparation occurs before a cognitive, motor or emotional response, and is detectable with EEG as a negatively polarized voltage shift.


Contingency is an associative connection between stimuli or responses that are usually paired within a short time period of milliseconds to seconds.

Classical conditioning

Classical conditioning, also called Pavlovian conditioning, is a learning process in which two stimuli are repeatedly paired until one elicits a reflexive behavioural or physiological response that relates to the other.

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Chaudhary, U., Birbaumer, N. & Ramos-Murguialday, A. Brain–computer interfaces for communication and rehabilitation. Nat Rev Neurol 12, 513–525 (2016).

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