[Abstract] Brain-computer interfaces for communication and rehabilitation : Nature Reviews Neurology


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.


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


  1. Wyrwicka, W. & Sterman, M. B. Instrumental conditioning of sensorimotor cortex EEG spindles in the waking cat. Physiol. Behav. 3, 703707 (1968).
  2. Kamiya, J. in Altered states of consciousness. (ed Tart, C.) 519529 (New York: Wiley, 1969).
  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, 179204 (1973).
  4. Vidal, J.-J. Toward direct brain-computer communication. Annu. Rev. Biophys. Bioeng. 2, 157180 (1973).
    The first paper describing a brain computer interface and the hypothetical learning mechanisms involved.
  5. Sterman, M. B., Wyrwicka, W. & Roth, S. Electrophysiological correlates and neural substrates of alimentary behavior in the cat. Ann. NY Acad. Sci. 157, 723739 (1969).
  6. Sterman, M. & Friar, L. Suppression of seizures in epileptic Following on sensorimotor EEG feedback training. Electroencephalogr. Clin. Neurophysiol. 33, 8995 (1972).
  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, 293306 (1976).
  8. Sterman, M. B. & Macdonald, L. R. Effects of central cortical EEG feedback training on incidence of poorly controlled seizures. Epilepsia 19, 207222 (1978).
  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, 664670 (1999).
  10. Donoghue, J. P. Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5, 10851088 (2002).
  11. Nicolelis, M. A. L. Actions from thoughts. Nature 409, 403407 (2001).
  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, 10981101 (2008).
  13. Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct Cortical Control of 3D Neuroprosthetic Devices. Sci. 296, 18291832 (2002).
  14. Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. & Shenoy, K. V. A high-performance brain-computer interface. Nature 442, 195198 (2006).
  15. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361365 (2000).
  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, 141142 (2002).
  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.

  18. Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164171 (2006).
  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, 603611 (2007).
  20. Birbaumer, N., Ramos Murguialday, A., Weber, C. & Montoya, P. Chapter 8 neurofeedback and brain-computer Interface: clinical applications. Int. Rev. Neurobiol. 86, 107117 (2009).
  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, 112 (2003).
  22. Monastra, V. J. et al. Electroencephalographic biofeedback in the treatment of attention-deficit / hyperactivity disorder. J. Neurother. 9, 534 (2006).
  23. Kotchoubey, B. et al. Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 42, 406416 (2001).
  24. Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 11421145 (2015).
  25. Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372375 (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.

  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, 111 (2014).
  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, 3336 (2003).
  28. Caria, A., Sitaram, R. & Birbaumer, N. Real-time fMRI: a tool for local brain regulation. Neuroscientist. 18, 487501 (2012).
  29. Chaudhary, U., Birbaumer, N. & Curado, M. R. Brain-machine interface (BMI) in paralysis. Ann. Phys. Rehabil. Med. 58, 913 (2015).
  30. Nijboer, F. et al. An auditory brain–computer interface (BCI). J. Neurosci. Methods 167, 4350 (2008).
  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, 112(2007).
  32. Lugo, Z. R. et al. A vibrotactile p300-based brain-computer interface for consciousness detection and communication. Clin. EEG Neurosci. 45, 1421 (2014).
  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, 13371355 (2012).
  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).
  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).
  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, 498506 (2004).
  37. Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neuronal population coding of movement direction. Science 233, 14161419 (1986).
  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, 29282937 (1988).
  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, 121142 (2002).
  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, 6371(2004).
  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, 495500 (2007).
  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, 807809 (2014).
  43. Birbaumer, N., Elbert, T., Canavan, A. & Rockstroh, B. Slow potentials of the cerebral cortex and behavior. Physiol. Rev. 70, 141 (1990).
  44. Kubler, A. et al. Brain-computer communication: self regulation of slow cortical potentials for verbal communication. Arch. Phys. Med. Rehabil. 82, 15331539 (2001).
  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, 120123 (2003).
  46. Pfurtscheller, G. & Aranibar, A. Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr. Clin. Neurophysiol. 46, 138146 (1979).
  47. Kübler, A. et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64, 17751777 (2005).
  48. Wolpaw, J. R. et al. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767791 (2002).
  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, 510523(1988).
  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, 90100 (2009).
  51. Halder, S. et al. An auditory oddball brain-computer interface for binary choices. Clin. Neurophysiol. 121, 516523 (2010).
  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, 270281 (2011).
  53. Sellers, E. W. & Donchin, E. A P300-based brain-computer interface: Initial tests by ALS patients. Clin. Neurophysiol. 117, 538548 (2006).
  54. Sellers, E. W., Vaughan, T. M. & Wolpaw, J. R. A brain-computer interface for long-term independent home use. Amyotroph. Lateral Scler. 11, 449455 (2010).
  55. Lesenfants, D. et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J. Neural Eng. Neural Eng. 11, 035002 (2014).
  56. Zhu, D., Bieger, J., Molina, G. G. & Aarts, R. M. A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. http://dx.doi.org/10.1155/2010/702357(2010).
  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, 381388 (2010).
  58. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150157 (2001).
  59. Birbaumer, N., Ruiz, S. & Sitaram, R. Learned regulation of brain metabolism. Trends Cogn. Sci. 17, 295302 (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.
  60. DeCharms, R. C. et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21, 436443 (2004).
  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, 123132 (2011).
  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, 577586 (2003).
  63. Yoo, S. S. et al. Brain computer interface using fMRI: spatial navigation by thoughts. Neuroreport 15, 15911595 (2004).
  64. Birbaumer, N. et al. Deficient fear conditioning in psychopathy: a functional magnetic resonance imaging study. Arch. Gen. Psychiatry 62, 799805 (2005).
  65. Linden, D. E. J. et al. Real-time self-regulation of emotion networks in patients with depression. PLoS One http://dx.doi.org/10.1371/journal.pone.0038115 (2012).
  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, 739748(2013).
  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, 197205 (2011).
  68. Chaudhary, U. et al. Motor response investigation in individuals with cerebral palsy using near infrared spectroscopy: pilot study. Appl. Opt. 53, 503510 (2014).
  69. Obrig, H. NIRS in clinical neurology – a ‘promising’ tool? Neuroimage 85, 535546 (2014).
  70. Gallegos-Ayala, G. et al. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology 82, 19301932 (2014).
    The first report of a controlled case study with BCI in a completely paralyzed, locked-in patient restoring communication.

  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, 10281037 (2007).
  72. Birbaumer, N. et al. A spelling device for the paralysed. Nature 398, 297298 (1999).
  73. Ramos-Murguialday, A. et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100108 (2014).
  74. Birbaumer, N., Murguialday, A. R. & Cohen, L. Brain-computer interface in paralysis. Curr. Opin. Neurol. 21, 634638 (2008).
  75. Chou, S. M. & Norris, F. H. Amyotrophic lateral sclerosis: Lower motor neuron disease spreading to upper motor neurons. Muscle Nerve 16, 864869 (1993).
  76. Bauer, G., Gerstenbrand, F. & Rumpl, E. Varieties of the Locked-in Syndrome. J. Neurol.221, 7791 (1979).
  77. Beukelman, D., Fager, S. & Nordness, A. Communication support for people with ALS. Neurol. Res. Int. 2011, 714693 (2011).
  78. Beukelman, D. & Mirenda, P. Augmentative & alternative communication: Supporting children & adults with complex communication needs. (Paul, H. Brookes, Baltimore, MD, 2005).
  79. Birbaumer, N. & Cohen, L. G. Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621636 (2007).
  80. Kennedy, P. R. & Bakay, R. A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9, 17071711 (1998).
  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, 198202(2000).
  82. Kennedy, P. et al. Using human extra-cortical local field potentials to control a switch. J. Neural Eng. 1, 7277 (2004).
  83. Wilhelm, B., Jordan, M. & Birbaumer, N. Communication in locked-in syndrome: effects of imagery on salivary pH. Neurology 67, 534535 (2006).
  84. Murguialday, A. R. et al. Transition from the locked in to the completely locked-in state: a physiological analysis. Clin. Neurophysiol. 122, 925933 (2011).
  85. Birbaumer, N. Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 43, 517532 (2006).
  86. Kübler, A. & Birbaumer, N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin. Neurophysiol. 119, 26582666 (2008).
  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, 1784917854 (2004).
  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, 12931303 (2010).
  89. Thorns, J. et al. Movement initiation and inhibition are impaired in amyotrophic lateral sclerosis. Exp. Neurol. 224, 389394 (2010).
  90. Birbaumer, N., Piccione, F., Silvoni, S. & Wildgruber, M. Ideomotor silence: the case of complete paralysis and brain-computer interfaces (BCI). Psychol. Res. 76, 183191 (2012).
  91. Hinterberger, T. et al. Neuronal mechanisms underlying control of a brain – computer interface. Eur. J. Neurosci. 21, 31693181 (2005).
  92. Hinterberger, T. et al. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy Behav. 13, 300306 (2008).
  93. Koralek, A. C. et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483, 331335 (2012).
  94. Dworkin, B. R. & Miller, N. E. Failure to replicate visceral learning in the acute curarized rat preparation. Behav. Neurosci. 100, 299314 (1986).
    This paper describes the failure to establish instrumental learning of physiological responses in the curarized rat and possible reasons for this problem.
  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, 541574(2010).
  96. Birbaumer, N. & Chaudhary, U. Learning from brain control: clinical application of brain–computer interfaces. e-Neuroforum 6, 8795 (2015).
  97. Furdea, A. et al. A new (semantic) reflexive brain-computer interface: in search for a suitable classifier. J. Neurosci. Methods 203, 233240 (2012).
  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, 18 (2013).
  99. De Massari, D. et al. Brain communication in the locked-in state. Brain 136, 19892000(2013).
  100. Lulé, D. et al. Brain responses to emotional stimuli in patients with amyotrophic lateral sclerosis (ALS). J. Neurol. 254, 519527 (2007).
  101. Lulé, D. et al. Life can be worth living in locked-in syndrome. Prog. Brain Res. 177, 339351(2009).
  102. Lulé, D. et al. Quality of life in fatal disease: the flawed judgement of the social environment. J. Neurol. 260, 28362843 (2013).
  103. Chaudhary, U. & Birbaumer, N. Communication in locked-in state after brainstem stroke: a brain- computer-interface approach. Ann. Transl. Med. 3, 24 (2015).
  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).
  105. Kübler, A. et al. Self-regulation of slow cortical potentials in completely paralyzed human patients. Neurosci. Lett. 252, 171174 (1998).
  106. Piccione, F. et al. P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin. Neurophysiol. 117, 531537 (2006).
  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).
  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, 476488 (2003).
  109. Young, J. & Forster, A. Review of stroke rehabilitation. BMJ 334, 8690 (2007).
  110. Saka, O., McGuire, A. & Wolfe, C. Cost of stroke in the United Kingdom. Age Ageing 38, 2732 (2008).
  111. Langhorne, P., Bernhardt, J. & Kwakkel, G. Stroke rehabilitation. Lancet 377, 16931702(2015).
  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, 16291637 (2002).
  113. Ward, N. S. & Cohen, L. G. Mechanisms underlying recovery of motor function after stroke. Arch. Neurol. 61, 18441848 (2004).
  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, 237251 (1999).
  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, 20952104(2006).
  116. Buch, E. R. et al. Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 135, 596614 (2012).
  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).
  118. Chollet, F. et al. Fluoxetine for motor recovery after acute ischaemic stroke (FLAME): a randomised placebo-controlled trial. Lancet Neurol. 10, 123130 (2011).
  119. Savitz, S. I. et al. Stem cells as an emerging paradigm in stroke 3: enhancing the development of clinical trials. Stroke 45, 634639 (2014).
  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, 662667(2011).
  121. Gulati, T. et al. Robust neuroprosthetic control from the stroke perilesional cortex. J. Neurosci. 35, 86538661 (2015).
  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, 13011309(2013).
    This paper describes the neurophysiological bases of BCI applications in spinal cord injury.

  123. Lucas, T. H. & Fetz, E. E. Myo-cortical crossed feedback reorganizes primate motor cortex output. J. Neurosci. 33, 52615274 (2013).
  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. http://dx.doi.org/10.3389/fneng.2014.00030 (2014).
  125. Ono, T. et al. Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front. Neuroeng. http://dx.doi.org/10.3389/fneng.2014.00019 (2014).
  126. Pichiorri, F. et al. Brain–computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 77, 851865 (2015).
  127. Kasahara, K., DaSalla, C. S., Honda, M. & Hanakawa, T. Neuroanatomical correlates of brain–computer interface performance. Neuroimage 110, 95100 (2015).
  128. Bensmaia, S. J. & Miller, L. E. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat. Rev. Neurosci. 15, 313325 (2014).
  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, 288296 (2014).
  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, 154159 (2015).
  131. Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557564 (2013).
  132. Ouzký, M. Towards concerted efforts for treating and curing spinal cord injury (Council of Europe Parliamentary Assembly document 9401). https://assembly.coe.int/nw/xml/XRef/X2H-Xref-ViewHTML.asp?FileID=9680&lang=en (2002)
  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, 184192 (2010).
  134. Wolpaw, J. R. The complex structure of a simple memory. Trends Neurosci. 20, 588594(1997).
  135. Wang, W. et al. An electrocorticographic brain interface in an individual with tetraplegia. PLoS ONE http://dx.doi.org/10.1371/journal.pone.0055344 (2013).
  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, 3336 (2003).
  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, 45974600 (2013).
  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, 318324 (2015).
  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, 215223 (2008).
  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).
  141. Pfurtscheller, G., Guger, C., Müller, G., Krausz, G. & Neuper, C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211214 (2000).
    The first paper demonstrating noninvasive brain control using a sensorimotor rhythm brain–computer interface in a high spinal cord patient.

  142. Courtine, G. & Bloch, J. Defining Ecological Strategies in Neuroprosthetics. Neuron 86, 2933 (2015).
  143. van den Brand, R. et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 336, 11821185 (2012).
  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 http://dx.doi.org/10.1371/journal.pone.0073691 (2013).
  145. Bardin, J. C. et al. Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain 134, 769782(2011).
  146. Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362, 579589 (2010).
  147. Schnakers, C. et al. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. Neurocase 15, 271277 (2009).
  148. Lulé, D. et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin. Neurophysiol. 124, 101106 (2013).

Source: Brain-computer interfaces for communication and rehabilitation : Nature Reviews Neurology : Nature Research


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