Skip to main content

Brain–Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma

  • Conference paper
Systems Neuroscience and Rehabilitation

Abstract

Paralysis after stroke or neurotrauma is among the leading causes of long term disability in adults. The development of brain–computer interface (BCI) systems that allow online classification of electric or metabolic brain activity and their translation into control signals of external devices or computers have led to two major approaches in tackling the problem of paralysis. While assistive BCI systems strive for continuous high-dimensional control of robotic devices or functional electric stimulation (FES) of paralyzed muscles to substitute for lost motor functions in a daily life environment (e.g. Velliste et al. 2008 [1]; Hochberg et al. 2006 [2]; Pfurtscheller et al. 2000 [3]), restorative BCI systems aim at normalization of ­neurophysiologic activity that might facilitate motor recovery (e.g. Birbaumer et al. 2007, 2009 [4, 5]; Daly et al. 2008 [6]). In order to make assistive BCI systems work in daily life, high BCI communication speed is necessary, an issue that by now can only be achieved by invasive recordings of brain activity (e.g. via multi-unit arrays, MUA, or electrocorticogram, ECoG). Restorative BCI systems, in contrast, were developed as training tools based on non-invasive methods such as electro- or magnetoencephalography (EEG/MEG). More recently developed approaches use real-time functional magnetic resonance imaging (rtfMRI) or near-infrared ­spectroscopy (NIRS). Here, we provide an overview of the current state in the development and application of assistive and restorative BCI and introduce novel approaches to improve BCI control with brain stimulation such as transcranial direct current stimulation (tDCS). The outlook of using BCI in rehabilitation of stroke and neurotrauma is discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.clinicaltrials.gov/ct2/show/NCT00912041.

References

  1. Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB (2008) Cortical control of a prosthetic arm for self-feeding. Nature 453:1098–1101

    PubMed  CAS  Google Scholar 

  2. Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neural ensemble control of prosthetic devices by a human with tetraplegia. Nature 442:164–171

    PubMed  CAS  Google Scholar 

  3. Pfurtscheller G, Guger C, Muller G, Krausz G, Neuper C (2000) Brain oscillations control hand orthosis in a tetraplegic. Neurosci Lett 292:211–214

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  5. Birbaumer N, Ramos Murguialday A, Weber C, Montoya P (2009) Neurofeedback and brain–computer interface clinical applications. Int Rev Neurobiol 86:107–117

    PubMed  Google Scholar 

  6. Daly JJ, Wolpaw JR (2008) Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 7:1032–1043

    PubMed  Google Scholar 

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

    Google Scholar 

  8. Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP (2002) Instant neural control of a movement signal. Nature 416:141–142

    PubMed  CAS  Google Scholar 

  9. Taylor DM, Tillery SI, Schwartz AB (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296:1829–1832

    PubMed  CAS  Google Scholar 

  10. Carmena JM, Lebedev MA, Crist RA, O’Doherty JA, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MAL (2003) Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol 1:1–16

    Google Scholar 

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

    PubMed  CAS  Google Scholar 

  12. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kubler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralyzed. Nature 398:297–298

    PubMed  CAS  Google Scholar 

  13. Pfurtscheller G, Graimann B, Huggins JE, Levine SP (2004) Brain–computer communication based on the dynamics of brain oscillations. Suppl Clin Neurophysiol 57:583–591

    PubMed  CAS  Google Scholar 

  14. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791

    PubMed  Google Scholar 

  15. McFarland DJ, Krusienski DJ, Sarnacki WA, Wolpaw JR (2008) Emulation of computer mouse control with a noninvasive brain–computer interface. J Neural Eng 5:101–110

    PubMed  Google Scholar 

  16. Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G (2007) The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37:539–550

    PubMed  Google Scholar 

  17. Sitaram R, Caria A, Birbaumer N (2009) Hemodynamic brain–computer interfaces for communication and rehabilitation. Neural Netw 22:1320–1328

    PubMed  Google Scholar 

  18. Kübler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, McFarland DJ, Birbaumer N, Wolpaw JR (2005) Patients with ALS can use sensorimotor rhythms to operate a brain–computer interface. Neurology 64:1775–1777

    PubMed  Google Scholar 

  19. Buch E, Weber C, Cohen LG, Braun C, Dimyan MA, Ard T, Mellinger J, Caria A, Soekadar S, Fourkas A, Birbaumer N (2008) Think to move: a neuromagnetic brain–computer interface (BCI) system for chronic stroke. Stroke 39:910–917

    PubMed  Google Scholar 

  20. Organization WH (2003) The World Health report: shaping the future. World Health Organization, Geneva, p 2003

    Google Scholar 

  21. Kwakkel G, Kollen BJ, van der Grond J, Prevo AJ (2003) Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke 34:2181–2186

    PubMed  Google Scholar 

  22. Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, Hailpern SM, Ho M, Howard V, Kissela B, Kittner S, Lloyd-Jones D, McDermott M, Meigs J, Moy C, Nichol G, O’Donnell C, Roger V, Sorlie P, Steinberger J, Thom T, Wilson M, Hong Y (2008) Heart disease and stroke statistics – 2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 117:e25–e146

    PubMed  Google Scholar 

  23. Wolf SL, Winstein CJ, Miller JP, Taub E, Uswatte G, Morris D, Giuliani C, Light KE, Nichols-Larsen D, EXCITE Investigators (2006) 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

    PubMed  CAS  Google Scholar 

  24. Luft AR, McCombe-Waller S, Whitall J, Forrester LW, Macko R, Sorkin JD, Schulz JB, Goldberg AP, Hanley DF (2004) Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial. JAMA 292:1853–1861

    PubMed  CAS  Google Scholar 

  25. Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Rupp R (2003) ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett 351:33–36

    PubMed  CAS  Google Scholar 

  26. Broetz D, Braun C, Weber C, Soekadar SR, Caria A, Birbaumer N (2010) Combination of brain–computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil Neural Repair 24:674–679

    PubMed  Google Scholar 

  27. Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, Braun C, Birbaumer N (2010) Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology 48:578–582

    Google Scholar 

  28. Nagaoka T, Sakatani K, Awano T, Yokose N, Hoshino T, Murata Y, Katayama Y, Ishikawa A, Eda H (2010) Development of a new rehabilitation system based on a brain–computer interface using near-infrared spectroscopy. Adv Exp Med Biol 662:497–503

    PubMed  Google Scholar 

  29. Daly JJ, Cheng R, Rogers J, Litinas K, Hrovat K, Dohring M (2009) Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther 33:203–211

    PubMed  Google Scholar 

  30. Fetz EE (1969) Operant conditioning of cortical unit activity. Science 163:955–958

    PubMed  CAS  Google Scholar 

  31. Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233:1416–1419

    PubMed  CAS  Google Scholar 

  32. Georgopoulos AP, Lurito JT, Petrides M, Schwartz AB, Massey JT (1989) Mental rotation of the neuronal population vector. Science 243:234–236

    PubMed  CAS  Google Scholar 

  33. Nicolelis MA (2003) Brain–machine interfaces to restore motor function and probe neural circuits. Nat Rev Neurosci 4:417–422

    PubMed  CAS  Google Scholar 

  34. Scherberger H, Jarvis MR, Andersen RA (2005) Cortical local field potentials encodes movement intentions in the posterior parietal cortex. Neuron 46:347–354

    PubMed  CAS  Google Scholar 

  35. Dickey AS, Suminski A, Amit Y, Hatsopoulos NG (2009) Single-unit stability using chronically implanted multielectrode arrays. J Neurophysiol 102:1331–1339

    PubMed  Google Scholar 

  36. Rousche PJ, Normann RA (1998) Chronic recording capability of the Utah intracortical electrode array in cat sensory cortex. J Neurosci Methods 82:1–15

    PubMed  CAS  Google Scholar 

  37. Fountas KN, Smith JR (2007) Subdural electrode-associated complications: a 20-year experience. Stereotact Funct Neurosurg 85:264–272

    PubMed  Google Scholar 

  38. Penfield W, Welch K (1951) The supplementary motor area of the cerebral cortex; a clinical and experimental study. AMA Arch Neurol Psychiatry 66:289–317

    PubMed  CAS  Google Scholar 

  39. Fulton JF (1934) A note on the definition of the motor and premotor areas. Brain 57:311–316

    Google Scholar 

  40. Fulton JF (1935) Definition of the ‘motor’ and ‘premotor’ areas. Brain 58:311–316

    Google Scholar 

  41. Dum RP, Strick PL (1991) The origin of corticospinal projections from the premotor areas in the frontal lobe. J Neurosci 11:667–689

    PubMed  CAS  Google Scholar 

  42. Vargas-Irwin CE, Shakhnarovich G, Yadollahpour P, Mislow JM, Black MJ, Donoghue JP (2010) Decoding complete reach and grasp actions from local primary motor cortex populations. J Neurosci 30:9659–9669

    PubMed  CAS  Google Scholar 

  43. Schalk G, Miller KJ, Anderson NR, Wilson JA, Smyth MD, Ojemann JG, Moran DW, Wolpaw JR, Leuthardt EC (2008) Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng 5:75–84

    PubMed  CAS  Google Scholar 

  44. Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW (2004) A brain–computer interface using electrocorticographic signals in humans. J Neural Eng 1:63–71

    PubMed  Google Scholar 

  45. Freeman WJ, Rogers LJ, Holmes MD, Silbergeld DL (2000) Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands. J Neurosci Methods 95:111–121

    PubMed  CAS  Google Scholar 

  46. Staba RJ, Wilson CL, Bragin A, Fried I, Engel J Jr (2002) Quantitative analysis of high-frequency oscillations (80–500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. J Neurophysiol 88:1743–1752

    PubMed  Google Scholar 

  47. Ball T, Kern M, Mutschler I, Aertsen A, Schulze-Bonhage A (2009) Signal quality of ­simultaneously recorded invasive and non-invasive EEG. Neuroimage 46:708–716

    PubMed  Google Scholar 

  48. Chao ZC, Nagasaka Y, Fujii N (2010) Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front Neuroeng 3:3

    PubMed  Google Scholar 

  49. Bradberry TJ, Gentili RJ, Contreras-Vidal JL (2010) Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. J Neurosci 30:3432–3437

    PubMed  CAS  Google Scholar 

  50. Waldert S, Preissl H, Demandt E, Braun C, Birbaumer N, Aertsen A, Mehring C (2008) Hand movement direction decoded from MEG and EEG. J Neurosci 28:1000–1008

    PubMed  CAS  Google Scholar 

  51. Lebedev MA, Nicolelis MA (2006) Brain–machine interfaces: past, present and future. Trends Neurosci 29:536–546

    PubMed  CAS  Google Scholar 

  52. Skinner F (1953) Science and human behavior. Macmillan, New York

    Google Scholar 

  53. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) Brain–computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8:164–173

    PubMed  CAS  Google Scholar 

  54. Kübler A, Kotchoubey B, Kaiser J, Wolpaw J, Birbaumer N (2001) Brain–computer ­communication: unlocking the locked-in. Psychol Bull 127:358–375

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  56. Birbaumer N, Hinterberger T, Kübler A, Neumann N (2003) The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans Neural Syst Rehabil Eng 11:120–123

    PubMed  Google Scholar 

  57. Perelmouter J, Birbaumer N (2000) A binary spelling interface with random errors. IEEE Trans Rehabil Eng 8:227–232

    PubMed  CAS  Google Scholar 

  58. Hinterberger T, Veit R, Wilhelm B, Weiskopf N, Vatine JJ, Birbaumer N (2005) Neuronal mechanisms underlying control of a brain–computer-interface. Eur J Neurosci 21:3169–3181

    PubMed  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  60. Gastaut H, Terzian H, Gastaut Y (1952) Study of a little electroencephalographic activity: rolandic arched rhythm. Mars Med 89:296–310

    PubMed  CAS  Google Scholar 

  61. Howe RC, Sterman MB (1972) Cortical–subcortical EEG correlates of suppressed motor behavior during sleep and waking in the cat. J Electroencephalogr Clin Neurophysiol 32:681–695

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  63. Leocani L, Toro C, Zhuang P, Gerloff C, Hallet M (2001) Event-related desynchronization in reaction time paradigms: a comparison with event-related potentials and corticospinal excitability. Clin Neurophysiol 112:923–930

    PubMed  CAS  Google Scholar 

  64. Pfurtscheller G, Stancák A Jr, Neuper C (1996) Event-related synchronization (ERS) in the alpha band – an electrophysiological correlate of cortical idling: a review. Int J Psychophysiol 24:39–46

    PubMed  CAS  Google Scholar 

  65. Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc Natl Acad Sci USA 101:17849–17854

    PubMed  CAS  Google Scholar 

  66. Wolpaw JR (2007) Brain–computer interfaces as new brain output pathways. J Physiol 579:613–619

    PubMed  CAS  Google Scholar 

  67. Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kübler A (2007) An MEG-based brain–computer interface (BCI). Neuroimage 36:581–593

    PubMed  Google Scholar 

  68. Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70:510–523

    PubMed  CAS  Google Scholar 

  69. Lenhardt A, Kaper M, Ritter HJ (2008) An adaptive P300-based online brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 16:121–130

    PubMed  Google Scholar 

  70. Weiskopf N, Veit R, Erb M, Mathiak K, Grodd W, Goebel R, Birbaumer N (2003) Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. NeuroImage 19:577–586

    PubMed  Google Scholar 

  71. Yoo SS, Fairneny T, Chen NK, Choo SE, Panych LP, Park H, Lee SY, Jolesz FA (2004) Brain–computer interface using fMRI: spatial navigation by thoughts. Neuroreport 15:1591–1595

    PubMed  Google Scholar 

  72. DeCharms RC, Maeda F, Glover GH, Ludlow D, Pauly JM, Soneji D, Gabrieli JD, Mackey SC (2005) Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci USA 102:18626–18631

    PubMed  CAS  Google Scholar 

  73. Caria A, Veit R, Sitaram R, Lotze M, Weiskopf N, Grodd W, Birbaumer N (2007) Regulation of anterior insular cortex activity using real-time fMRI. NeuroImage 35:1238–1246

    PubMed  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  75. Elbert T, Rockstroh B, Lutzenberger W, Birbaumer N (1984) Self-regulation of the brain and behavior. Springer, New York

    Google Scholar 

  76. Seifert AR, Lubar JF (1975) Reduction of epileptic seizures through EEG biofeedback training. Biol Psychol 3:157–184

    PubMed  CAS  Google Scholar 

  77. Kotchoubey B, Strehl U, Uhlmann C, Holzapfel S, König M, Fröscher W, Blankenhorn V, Birbaumer N (2001) Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 42:406–416

    PubMed  CAS  Google Scholar 

  78. Birbaumer N, Elbert T, Rockstroh B, Lutzenberger W (1986) Biofeedback of slow cortical potentials in attentional disorders. In: McCallum WC, Zappoli R, Denoth F (eds) Cerebral psychophysiology: studies in event-related potentials. Elsevier, Amsterdam

    Google Scholar 

  79. Strehl U, Leins U, Goth G, Klinger C, Hinterberger T, Birbaumer N (2006) Self-regulation of slow cortical potentials: a new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics 118:1530–1540

    Google Scholar 

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

    PubMed  Google Scholar 

  81. Lotze M, Grodd W, Birbaumer N, Erb M, Huse E, Flor H (1999) Does use of a myoelectric prosthesis prevent cortical reorganization and phantom limb pain? Nat Neurosci 2:501–502

    PubMed  CAS  Google Scholar 

  82. Chklovskii DB, Mel BW, Svoboda K (2004) Cortical rewiring and information storage. Nature 431:782–788

    PubMed  CAS  Google Scholar 

  83. Frost SB, Barbay S, Friel KM, Plautz EJ, Nudo RJ (2003) Reorganization of remote cortical regions after ischemic brain injury: a potential substrate for stroke recovery. J Neurophysiol 89:3205–3214

    PubMed  CAS  Google Scholar 

  84. Murase N, Duque J, Mazzocchio R, Cohen LG (2004) Influence of interhemispheric interactions on motor function in chronic stroke. Ann Neurol 55:400–409

    PubMed  Google Scholar 

  85. Duque J, Hummel F, Celnik P, Murase N, Mazzocchio R, Cohen LG (2005) Transcallosal inhibition in chronic subcortical stroke. Neuroimage 28:940–946

    PubMed  Google Scholar 

  86. Grefkes C, Nowak DA, Eickhoff SB, Dafotakis M, Küst J, Karbe H, Fink GR (2008) Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol 63:236–246

    PubMed  Google Scholar 

  87. Harris-Love ML, Perez MA, Chen R, Cohen LG (2007) Interhemispheric inhibition in distal and proximal arm representations in the primary motor cortex. J Neurophysiol 97:2511–2515

    PubMed  Google Scholar 

  88. Floel A, Nagorsen U, Werhahn KJ, Ravindran S, Birbaumer N, Knecht S, Cohen LG (2004) Influence of somatosensory input on motor function in patients with chronic stroke. Ann Neurol 56:206–212

    PubMed  Google Scholar 

  89. Conforto AB, Kaelin-Lang A, Cohen LG (2002) Increase in hand muscle strength of stroke patients after somatosensory stimulation. Ann Neurol 51:122–125

    PubMed  Google Scholar 

  90. Scheidtmann K (2004) Advances in adjuvant pharmacotherapy for motor rehabilitation: effects of levodopa. Restor Neurol Neurosci 22:393–398

    PubMed  Google Scholar 

  91. Liu KP, Chan CC, Wong RS, Kwan IW, Yau CS, Li LS, Lee TM (2009) A randomized controlled trial of mental imagery augment generalization of learning in acute poststroke patients. Stroke 40:2222–2225

    PubMed  Google Scholar 

  92. Malouin F, Richards CL, Doyon J, Desrosiers J, Belleville S (2004) Training mobility tasks after stroke with combined mental and physical practice: a feasibility study. Neurorehabil Neural Repair 18:66–75

    PubMed  Google Scholar 

  93. Page SJ, Levine P, Leonard A (2007) Mental practice in chronic stroke: results of a randomized, placebo-controlled trial. Stroke 38:1293–1297

    PubMed  Google Scholar 

  94. Hummel F, Celnik P, Giraux P, Floel A, Wu WH, Gerloff C, Cohen LG (2005) Effects of non-invasive cortical stimulation on skilled motor function in chronic stroke. Brain 128:490–499

    PubMed  Google Scholar 

  95. Hesse S, Werner C, Schonhardt EM, Bardeleben A, Jenrich W, Kirker SG (2007) Combined transcranial direct current stimulation and robot-assisted arm training in subacute stroke patients: a pilot study. Restor Neurol Neurosci 25:9–15

    PubMed  CAS  Google Scholar 

  96. Takeuchi N, Chuma T, Matsuo Y, Watanabe I, Ikoma K (2005) Repetitive transcranial magnetic stimulation of contralesional primary motor cortex improves hand function after stroke. Stroke 36:2681–2686

    PubMed  Google Scholar 

  97. Platz T, Kim IH, Engel U, Kieselbach A, Mauritz KH (2002) Brain activation pattern as assessed with multi-modal EEG analysis predict motor recovery among stroke patients with mild arm paresis who receive the arm ability training. Restor Neurol Neurosci 20:21–35

    PubMed  CAS  Google Scholar 

  98. Calautti C, Naccarato M, Jones PS, Sharma N, Day DD, Carpenter AT, Bullmore ET, Warburton EA, Baron JC (2007) The relationship between motor deficit and hemisphere ­activation balance after stroke: a 3 T fMRI study. Neuroimage 34:322–331

    PubMed  Google Scholar 

  99. Ward NS, Cohen LG (2004) Mechanisms underlying recovery of motor function after stroke. Arch Neurol 61:1844–1848

    PubMed  Google Scholar 

  100. Taub E, Uswatte G, Mark VW, Morris DM (2006) The learned nonuse phenomenon: implications for rehabilitation. Eura Medicophys 42:241–256

    PubMed  CAS  Google Scholar 

  101. Buch ER, Fourkas AD, Weber C, Birbaumer N, Cohen LG (2010) Anatomical parieto-frontal connectivity predicts performance gains in μ rhythm-based brain–computer interface (BCI) training in chronic stroke. SFN 2010, San Diego, 493.6/FFF19

    Google Scholar 

  102. Ang KK, Guan C, Chua KS, Ang BT, Kuah C, Wang C, Phua KS, Chin ZY, Zhang H (2010) Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain–computer interface with robotic feedback. Conf Proc IEEE Eng Med Biol Soc 1:5549–5552

    Google Scholar 

  103. Nitsche MA, Paulus W (2000) Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol 527:633–639

    PubMed  CAS  Google Scholar 

  104. Nitsche MA, Schauenburg A, Lang N, Liebetanz D, Exner C, Paulus W, Tergau F (2003) Facilitation of implicit motor learning by weak transcranial direct current stimulation of the primary motor cortex in the human. J Cognit Neurosci 15:619–626

    Google Scholar 

  105. Reis J, Schambra HM, Cohen LG, Buch ER, Fritsch B, Zarahn E, Celnik PA, Krakauer JW (2009) Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc Natl Acad Sci USA 106:1590–1595

    PubMed  CAS  Google Scholar 

  106. Antal A, Nitsche MA, Kincses TZ, Kruse W, Hoffmann KP, Paulus W (2004) Facilitation of visuo-motor learning by transcranial direct current stimulation of the motor and extrastriate visual areas in humans. Eur J Neurosci 19:2888–2892

    PubMed  Google Scholar 

  107. Boggio PS, Nunes A, Rigonatti SP, Nitsche MA, Pascual-Leone A, Fregni F (2007) Repeated sessions of noninvasive brain DC stimulation is associated with motor function improvement in stroke patients. Restor Neurol Neurosci 25:123–129

    PubMed  Google Scholar 

  108. Miniussi C, Bonato C, Bignotti S, Gazzoli A, Gennarelli M, Pasqualetti P, Tura GB, Ventriglia M, Rossini PM (2005) Repetitive transcranial magnetic simulation (rTMS) at high and low frequency: an efficacious therapy for major drug-resistant depression? Clin Neurophysiol 116:1062–1071

    PubMed  CAS  Google Scholar 

  109. Lefaucheur JP (2004) Transcranial magnetic stimulation in the management of pain. Clin Neurophysiol (Suppl) 57:737–748

    Google Scholar 

  110. Theodore WH (2003) Transcranial magnetic stimulation in epilepsy. Epilepsy Curr 3:191–197

    PubMed  Google Scholar 

  111. Fregni F, Simon DK, Wu A, Pascual-Leone A (2005) Non-invasive brain stimulation for Parkinson’s disease: a systematic review and meta-analysis of the literature. J Neurol Neurosurg Psychiatry 76:1614–1623

    PubMed  CAS  Google Scholar 

  112. Mansur CG, Fregni F, Boggio PS, Riberto M, Gallucci-Neto J, Santos CM, Wagner T, Rigonatti SP, Marcolin MA, Pascual-Leone A (2005) A sham stimulation-controlled trial of rTMS of the unaffected hemisphere in stroke patients. Neurology 64:1802–1804

    PubMed  CAS  Google Scholar 

  113. Takeuchi N, Tada T, Toshima M, Chuma T, Matsuo Y, Ikoma K (2008) Inhibition of the unaffected motor cortex by 1 Hz repetitive transcranical magnetic stimulation enhances motor performance and training effect of the paretic hand in patients with chronic stroke. J Rehabil Med 40:298–303

    PubMed  Google Scholar 

  114. Fregni F, Boggio PS, Valle AC, Rocha RR, Duarte J, Ferreira MJ, Wagner T, Fecteau S, Rigonatti SP, Riberto M, Freedman SD, Pascual-Leone A (2006) A sham-controlled trial of a 5-day course of repetitive transcranial magnetic stimulation of the unaffected hemisphere in stroke patients. Stroke 37:2115–2122

    PubMed  Google Scholar 

  115. Khedr EM, Ahmed MA, Fathy N, Rothwell JC (2005) Therapeutic trial of repetitive transcranial magnetic stimulation after acute ischemic stroke. Neurology 65:466–468

    PubMed  Google Scholar 

  116. Kim YH, You SH, Ko MH, Park JW, Lee KH, Jang SH, Yoo WK, Hallett M (2006) Repetitive transcranial magnetic stimulation-induced corticomotor excitability and associated motor skill acquisition in chronic stroke. Stroke 37:1471–1476

    PubMed  Google Scholar 

  117. Pfurtscheller G, Klimesch W (1992) Functional topography during a visuoverbal judgment task studied with event-related desynchronization mapping. J Clin Neurophysiol 9:120–131

    PubMed  CAS  Google Scholar 

  118. Diwakar M, Huang MX, Srinivasan R, Harrington DL, Robb A, Angeles A, Muzzatti L, Pakdaman R, Song T, Theilmann RJ, Lee RR (2011) Dual-core beamformer for obtaining highly correlated neuronal networks in MEG. Neuroimage 54:253–263

    PubMed  Google Scholar 

Download references

Acknowledgements

This contribution was supported by the NINDS intramural research program of the National Institutes of Health (NIH), the Deutsche Forschungsgemeinschaft (DFG) and the German Ministry of Education and Research (BMBF, 01GQ0831).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leonardo G. Cohen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer

About this paper

Cite this paper

Soekadar, S.R., Birbaumer, N., Cohen, L.G. (2011). Brain–Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma. In: Kansaku, K., Cohen, L.G. (eds) Systems Neuroscience and Rehabilitation. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54008-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-54008-3_1

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-53998-8

  • Online ISBN: 978-4-431-54008-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics