Skip to main content
SpringerLink
Log in

Brain–Machine Interfaces in Stroke Neurorehabilitation

  • Chapter
  • First Online:
Clinical Systems Neuroscience

Abstract

Stroke is one of the leading causes for severe adult long-term disability. The number of people who depend on assistance in their daily life activities has drastically increased over the last years and will further accumulate due to demographic factors. Besides impact on cognitive and affective brain function, motor paralysis is the heaviest burden of stroke. While recent studies demonstrated the human brain’s remarkable capacity to reorganize and restore function under effective learning conditions, most rehabilitation strategies require residual movements that, however, are lacking in up to 30–50 % of stroke survivors. For these patients, there is currently no standardized or accepted treatment strategy. Recently it was shown that brain–machine interfaces (BMI) translating electric or metabolic brain signals into control signals of computers or machines provide two strategies that play an increasing role for the recovery of these stroke survivors’ motor function: first, assistive BMIs striving for continuous high-dimensional brain control of robotic devices or functional electric stimulation (FES) to assist in performing daily life activities and, second, rehabilitative BMIs aiming at augmentation of neuroplasticity facilitating recovery of brain function. Recent demonstrations of such assistive and rehabilitative BMI system’s clinical applicability, safety, and efficacy suggest that BMIs will play a substantial role in rehabilitation strategies for severe motor paralysis after stroke.

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

Access this chapter

Log in via an institution
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 159.00
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

References

  1. Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, Moran AE, Sacco RL, Anderson L, Truelsen T, O’Donnell M, Venketasubramanian N, Barker-Collo S, Lawes CM, Wang W, Shinohara Y, Witt E, Ezzati M, Naghavi M, Murray C (2014) Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010) and the GBD Stroke Experts Group. Lancet 383:245–254

    Article  PubMed Central  PubMed  Google Scholar 

  2. Gustavsson A, Svensson M, Jacobi F, Allgulander C, Alonso J, Beghi E, Dodel R, Ekman M, Faravelli C, Fratiglioni L, Gannon B, Jones DH, Jennum P, Jordanova A, Jönsson L, Karampampam K, Knappm M, Kobelt G, Kurthm T, Lieb R, Linde M, Ljungcrantz C, Maercker A, Melin B, Moscarelli M, Musayev A, Norwood F, Preisig M, Pugliatti M, Rehm J, Salvador-Carulla L, Schlehofer B, Simon R, Steinhausen HC, Stovner LJ, Vallat JM, Van den Bergh P, van Os J, Vos P, Xu W, Wittchen HU, Jönsson B, Olesen J, CDBE2010Study Group (2011) Cost of disorders of the brain in Europe 2010. Eur Neuropsychopharmacol 21:718–779

    Article  CAS  PubMed  Google Scholar 

  3. Birbeck GL, Hanna MG, Griggs RC (2014) Global opportunities and challenges for clinical neuroscience. JAMA 311:1609–1610

    Article  CAS  PubMed  Google Scholar 

  4. 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

    Article  PubMed  Google Scholar 

  5. Vidal JJ (1973) Toward direct brain–computer communication. Annu Rev Biophys Bioeng 2:157–180

    Article  CAS  PubMed  Google Scholar 

  6. Birbaumer N, Murguialday AR, Cohen L (2008) Brain–computer interface in paralysis. Curr Opin Neurol 21:634–638

    Article  PubMed  Google Scholar 

  7. Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, Piccione F, Birbaumer N (2011) Brain–computer interface in stroke: a review of progress. Clin EEG Neurosci 42:245–252

    Article  PubMed  Google Scholar 

  8. Miller NE (1969) Learning of visceral and glandular responses. Science 163:434–445

    Article  CAS  PubMed  Google Scholar 

  9. Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, McMorland AJ, Velliste M, Boninger ML, Schwartz AB (2013) High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381:557–564

    Article  PubMed Central  PubMed  Google Scholar 

  10. Moritz CT, Perlmutter SI, Fetz EE (2008) Direct control of paralysed muscles by cortical neurons. Nature 456:639–642

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Pohlmeyer EA, Oby ER, Perreault EJ, Solla SA, Kilgore KL, Kirsch RF, Miller LE (2009) Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles. PLoS One 4:e5924

    Article  PubMed Central  PubMed  Google Scholar 

  12. Ethier C, Oby ER, Bauman MJ, Miller LE (2012) Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 485:368–371

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. McGie SC, Zariffa J, Popovic MR, Nagai MK (2014) Short-term neuroplastic effects of brain-controlled and muscle-controlled electrical stimulation. Neuromodulation (in press). doi:10.1111/ner.12185

  14. 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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  16. Dimyan MA, Cohen LG (2011) Neuroplasticity in the context of motor rehabilitation after stroke. Nat Rev Neurol 7:76–85

    Article  PubMed  Google Scholar 

  17. Dobkin BH (2007) Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol 579:637–642

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Pfurtscheller G, Brunner C, Schlögl A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31:153–159

    Article  CAS  PubMed  Google Scholar 

  19. Soekadar SR, Witkowski M, Mellinger J, Ramos A, Birbaumer N, Cohen LG (2011) ERD-based online brain–machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance. IEEE Trans Neural Syst Rehabil Eng 19:542–549

    Article  PubMed  Google Scholar 

  20. Soekadar SR, Witkowski M, Birbaumer N, Cohen LG (2014) Enhancing Hebbian learning to control brain oscillatory activity. Cereb Cortex Neuromodulation (in press). doi:10.1093/cercor/bhu043

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

    Article  CAS  PubMed  Google Scholar 

  22. 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

    Article  CAS  PubMed  Google Scholar 

  23. Sakurada T, Kawase T, Takano K, Komatsu T, Kansaku K (2013) A BMI-based occupational therapy assist suit: asynchronous control by SSVEP. Front Neurosci 7:172

    Article  PubMed Central  PubMed  Google Scholar 

  24. 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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  26. Rea M, Rana M, Lugato N, Terekhin P, Gizzi L, Brötz D, Fallgatter A, Birbaumer N, Sitaram R, Caria A (2014) Lower limb movement preparation in chronic stroke: a pilot study toward an fNIRS-BCI for gait rehabilitation. Neurorehabil Neural Repair 28(6):564–575

    Article  PubMed  Google Scholar 

  27. Hwang EJ, Andersen RA (2009) Brain control of movement execution onset using local field potentials in posterior parietal cortex. J Neurosci 29:14363–14370

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Flint RD, Wright ZA, Scheid MR, Slutzky MW (2013) Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J Neural Eng 10:056005

    Article  PubMed Central  PubMed  Google Scholar 

  29. 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

    Article  PubMed  Google Scholar 

  30. Schalk G, Brunner P, Gerhardt LA, Bischof H, Wolpaw JR (2008) Brain–computer interfaces (BCIs): detection instead of classification. J Neurosci Methods 167:51–62

    Article  CAS  PubMed  Google Scholar 

  31. Wang W, Collinger JL, Degenhart AD, Tyler-Kabara EC, Schwartz AB, Moran DW, Weber DJ, Wodlinger B, Vinjamuri RK, Ashmore RC et al (2013) An electrocorticographic brain interface in an individual with tetraplegia. PLoS One 8:e55344

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  34. Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, 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:e42

    Article  PubMed Central  PubMed  Google Scholar 

  35. Birbaumer N, Gallegos-Ayala G, Wildgruber M, Silvoni S, Soekadar SR (2014) Direct brain control and communication in paralysis. Brain Topogr 27:4–11

    Article  PubMed  Google Scholar 

  36. Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, van der Smagt P, Donoghue JP (2012) Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485:372–375

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Sterman MB, Wyrwicka W, Howe R (1969) Behavioral and neurophysiological studies of the sensorimotor rhythm in the cat. Electroencephalogr Clin Neurophysiol 27:678–679

    CAS  PubMed  Google Scholar 

  38. Sterman MB, Macdonald LR (1978) Effects of central cortical EEG feedback training on incidence of poorly controlled seizures. Epilepsia 19:207–222

    Article  CAS  PubMed  Google Scholar 

  39. Lubar JF, Shouse MN (1976) 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

    Article  CAS  PubMed  Google Scholar 

  40. Monastra VJ, Lynn S, Linden M, Lubar JF, Gruzelier J, LaVaque TJ (2005) Electroencephalographic biofeedback in the treatment of attention-deficit/hyperactivity disorder. Appl Psychophysiol Biofeedback 30:95–114

    Article  PubMed  Google Scholar 

  41. 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:e1530–e1540

    Article  PubMed  Google Scholar 

  42. Linden DE, Habes I, Johnston SJ, Linden S, Tatineni R, Subramanian L, Sorger B, Healy D, Goebel R (2012) Real-time self-regulation of emotion networks in patients with depression. PLoS One 7:e38115

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Rozelle GR, Budzynski TH (1995) Neurotherapy for stroke rehabilitation: a single case study. Biofeedback Self Regul 20:211–228

    Article  CAS  PubMed  Google Scholar 

  44. Platz T, Kim IH, Engel U, Kieselbach A, Mauritz K-H (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

    CAS  PubMed  Google Scholar 

  45. Calautti C, Jones PS, Naccarato M, Sharma N, Day DJ, Bullmore ET, Warburton EA, Baron JC (2010) The relationship between motor deficit and primary motor cortex hemispheric activation balance after stroke: longitudinal fMRI study. J Neurol Neurosurg Psychiatry 81:788–792

    Article  CAS  PubMed  Google Scholar 

  46. Basmajian JV (1981) Biofeedback in rehabilitation: a review of principles and practices. Arch Phys Med Rehabil 62:469–475

    CAS  PubMed  Google Scholar 

  47. Basmajian JV, Gowland C, Brandstater ME, Swanson L, Trotter J (1982) EMG feedback treatment of upper limb in hemiplegic stroke patients: a pilot study. Arch Phys Med Rehabil 63:613–616

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  50. Prasad G, Herman P, Coyle D, McDonough S, Crosbie J (2010) Applying a brain–computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J Neuroeng Rehabil 7:60

    Article  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  52. 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

    Article  PubMed  Google Scholar 

  53. Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, Braun C, Birbaumer N (2011) Chronic stroke recovery after combined BCI training and physiotherapy: a case report. J Psychophysiol 48:578–582

    Article  Google Scholar 

  54. 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

    Article  PubMed  Google Scholar 

  55. Wang W, Collinger JL, Perez MA, Tyler-Kabara EC, Cohen LG, Birbaumer N, Brose SW, Schwartz AB, Boninger ML, Weber DJ (2010) Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. Phys Med Rehabil Clin N Am 21:157–178

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Ramos-Murguialday A, Broetz D, Rea M, Läer L, Yilmaz O, Brasil FL, Liberati G, Curado MR, Garcia-Cossio E, Vyziotis A, Cho W, Agostini M, Soares E, Soekadar SR, Caria A, Cohen LG, Birbaumer N (2013) Brain–machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol 74:100–108

    Article  PubMed Central  PubMed  Google Scholar 

  57. Brasil F, Curado MR, Witkowski M, Garcia E, Broetz D, Birbaumer N, Soekadar SR (2012) MEP predicts motor recovery in chronic stroke patients undergoing 4-weeks of daily physical therapy. Human brain mapping annual meeting, Beijing, June 10–14

    Google Scholar 

  58. Shaikhouni A, Donoghue JP, Hochberg LR (2013) Somatosensory responses in a human motor cortex. J Neurophysiol 109:2192–2204

    Article  PubMed Central  PubMed  Google Scholar 

  59. Ang KK, Chua KS, Phua KS, Wang C, Chin ZY, Kuah CW, Low W, Guan C (2014) A randomized controlled trial of EEG-based motor imagery brain–computer interface robotic rehabilitation for stroke. Clin EEG Neurosci (in press)

    Google Scholar 

  60. Várkuti B, Guan C, Pan Y, Phua KS, Ang KK, Kuah CW, Chua K, Ang BT, Birbaumer N, Sitaram R (2013) Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair 27:53–62

    Article  PubMed  Google Scholar 

  61. Mukaino M, Ono T, Shindo K, Fujiwara T, Ota T, Kimura A, Liu M, Ushiba J (2014) Efficacy of brain–computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. J Rehabil Med 46:378–382

    Article  PubMed  Google Scholar 

  62. Sitaram R, Veit R, Stevens B, Caria A, Gerloff C, Birbaumer N, Hummel F (2012) Acquired control of ventral premotor cortex activity by feedback training: an exploratory real-time FMRI and TMS study. Neurorehabil Neural Repair 26:256–265

    Article  PubMed  Google Scholar 

  63. Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R (2013) Neurofeedback-mediated self-regulation of the dopaminergic midbrain. NeuroImage 83:817–825

    Article  CAS  PubMed  Google Scholar 

  64. Ruiz S, Buyukturkoglu K, Rana M, Birbaumer N, Sitaram R (2014) Real-time fMRI brain computer interfaces: self-regulation of single brain regions to networks. Biol Psychol 95:4–20

    Article  PubMed  Google Scholar 

  65. Buch ER, Modir Shanechi A, Fourkas AD, Weber C, Birbaumer N, Cohen LG (2012) Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 135:596–614

    Article  PubMed Central  PubMed  Google Scholar 

  66. Dayan E, Censor N, Buch ER, Sandrini M, Cohen LG (2013) Noninvasive brain stimulation: from physiology to network dynamics and back. Nat Neurosci 16:838–844

    Article  CAS  PubMed  Google Scholar 

  67. Bolwig TG (2014) Neuroimaging and electroconvulsive therapy: a review. J ECT 30:138–142

    Article  PubMed  Google Scholar 

  68. Reis J, Robertson E, Krakauer JW, Rothwell J, Marshall L, Gerloff C, Wassermann E, Pascual-Leone A, Hummel F, Celnik PA, Classen J, Floel A, Ziemann U, Paulus W, Siebner HR, Born J, Cohen LG (2008) Consensus: “Can tDCS and TMS enhance motor learning and memory formation?”. Brain Stimul 1:363–369

    Article  PubMed Central  Google Scholar 

  69. Marshall L, Mölle M, Hallschmid M, Born J (2004) Transcranial direct current stimulation during sleep improves declarative memory. J Neurosci 24:9985–9992

    Article  CAS  PubMed  Google Scholar 

  70. Hummel FC, Voller B, Celnik P, Floel A, Giraux P, Gerloff C, Cohen LG (2006) Effects of brain polarization on reaction times and pinch force in chronic stroke. BMC Neurosci 7:73

    Article  PubMed Central  PubMed  Google Scholar 

  71. Khedr EM, Etraby AE, Hemeda M, Nasef AM, Razek AA (2010) Long-term effect of repetitive transcranial magnetic stimulation on motor function recovery after acute ischemic stroke. Acta Neurol Scand 121:30–37

    Article  CAS  PubMed  Google Scholar 

  72. 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

    Article  PubMed  Google Scholar 

  73. Sung WH, Wang CP, Chou CL, Chen YC, Chang YC, Tsai PY (2013) Efficacy of coupling inhibitory and facilitatory repetitive transcranial magnetic stimulation to enhance motor recovery in hemiplegic stroke patients. Stroke 44:1375–1382

    Article  PubMed  Google Scholar 

  74. Stagg CJ, Jayaram G, Pastor D, Kincses ZT, Matthews PM, Johansen-Berg H (2011) Polarity and timing-dependent effects of transcranial direct current stimulation in explicit motor learning. Neuropsychologia 49:800–804

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  75. Volpato C, Cavinato M, Piccione F, Garzon M, Meneghello F, Birbaumer N (2013) Transcranial direct current stimulation (tDCS) of Broca’s area in chronic aphasia: a controlled outcome study. Behav Brain Res 247:211–216

    Article  PubMed  Google Scholar 

  76. Soekadar SR, Witkowski M, Garcia-Cossio E, Birbaumer N, Cohen LG (2014) Learned EEG-based brain self-regulation of motor-related oscillations during application of transcranial electric brain stimulation: feasibility and limitations. Front Behav Neurosci 8:93

    Article  PubMed Central  PubMed  Google Scholar 

  77. Soekadar SR, Witkowski M, Garcia-Cossio E, Birbaumer N, Robinson SE, Cohen LG (2013) In vivo assessment of human brain oscillations during application of transcranial electric currents. Nat Commun 4:2032

    Article  PubMed  Google Scholar 

  78. Soekadar SR, Witkowski M, Robinson SE, Birbaumer N (2013) Combining electric brain stimulation and source-based brain–machine interface (BMI) training in neurorehabilitation of chronic stroke. J Neurol Sci 333:e542

    Article  Google Scholar 

  79. Soekadar SR, Birbaumer N, Slutzky MW, Cohen LG (2015) Brain-Machine Interfaces In Neurorehabilitation of Stroke. Neurobiology of Disease (in press)

    Google Scholar 

  80. Schalk G, Kubánek J, Miller KJ, Anderson NR, Leuthardt EC, Ojemann JG, Limbrick D, Moran D, Gerhardt LA, Wolpaw JR (2007) Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng 4:264–275

    Article  CAS  PubMed  Google Scholar 

  81. Mehring C, Nawrot MP, de Oliveira SC, Vaadia E, Schulze-Bonhage A, Aertsen A, Ball T (2004) 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

    Article  PubMed  Google Scholar 

  82. Stark E, Abeles M (2007) Predicting movement from multiunit activity. J Neurosci 27:8387–8394

    Article  CAS  PubMed  Google Scholar 

  83. Flint RD, Lindberg EW, Jordan LR, Miller LE, Slutzky MW (2012) Accurate decoding of reaching movements from field potentials in the absence of spikes. J Neural Eng 9:046006

    Article  PubMed Central  PubMed  Google Scholar 

  84. Silvoni S, Cavinato M, Volpato C, Cisotto G, Genna C, Agostini M, Turolla A, Ramos-Murguialday A, Piccione F (2013) Kinematic and neurophysiological consequences of an assisted-force-feedback brain–machine interface training: a case study. Front Neurol 4:173

    Article  PubMed Central  PubMed  Google Scholar 

  85. Guggenmos DJ, Azin M, Barbay S, Mahnken JD, Dunham C, Mohseni P, Nudo RJ (2013) Restoration of function after brain damage using a neural prosthesis. PNAS 110:21177–21182

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  86. Nishimura Y, Perlmutter SI, Fetz EE (2013) Restoration of upper limb movement via artificial corticospinal and musculospinal connections in a monkey with spinal cord injury. Front Neural Circuits 7:57

    Article  PubMed Central  PubMed  Google Scholar 

  87. Bundy DT, Wronkiewicz M, Sharma M, Moran DW, Corbetta M, Leuthardt EC (2012) Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain–computer interfaces in hemiplegic stroke survivors. J Neural Eng 9:036011

    Article  PubMed Central  PubMed  Google Scholar 

  88. Carmena JM (2013) Advances in neuroprosthetic learning and control. PLoS Biol 11:e1001561

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  89. Dipietro L, Krebsa HI, Fasoli SE, Volpe BT, Hogan N (2009) Submovement changes characterize generalization of motor recovery after stroke. Cortex 45:318–324

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the Intramural Research Program (IRP) of the National Institute of Neurological Disorders and Stroke (NINDS), USA; the German Federal Ministry of Education and Research (BMBF, grant number 01GQ0831, 16SV5838K to SRS and NB); the BMBF to the German Center for Diabetes Research (DZD e.V., grand number 01GI0925), the Deutsche Forschungsgemeinschaft (DFG, grant number SO932-2 to SRS and Reinhart Koselleck support to NB); the European Commission under the project WAY (grant number 288551 to SRS and NB); and the Volkswagenstiftung (VW) and the Baden-Württemberg Stiftung, Germany. This chapter is an updated and shortened version of “Brain–Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma” published in the first edition of Systems Neuroscience and Rehabilitation and the most recent review article on this topic [79] by the same authors.

Author information

Authors and Affiliations

  1. Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstr. 5, 72076, Tübingen, Germany

    Surjo R. Soekadar M.D. & Niels Birbaumer

  2. Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Calwerstr. 14, 72076, Tübingen, Germany

    Surjo R. Soekadar M.D.

  3. Ospedale San Camillo, IRCCS, Venice, Italy

    Stefano Silvoni & Niels Birbaumer

  4. Human Cortical Physiology and Neurorehabilitation Section (HCPS), NINDS, NIH, Building 10, Room 7D54, 20892, Bethesda, MD, USA

    Leonardo G. Cohen

  5. Institute of Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Otfried-Müller-Str. 10, 72076, Tübingen, Germany

    Niels Birbaumer

Authors
  1. Surjo R. Soekadar M.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefano Silvoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonardo G. Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Niels Birbaumer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Surjo R. Soekadar M.D. .

Editor information

Editors and Affiliations

  1. Systems Neuroscience Section, Department of Rehabilitation for Brain Functions, Research Institute of National, Rehabilitation Center for Persons with Disabilities, Tokorozawa, Japan

    Kenji Kansaku

  2. Human Cortical Physiology and Stroke Neu, National Institute of Neurological Disor National Institutes of Health, Bethesda, Maryland, USA

    Leonardo G. Cohen

  3. Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany

    Niels Birbaumer

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Japan

About this chapter

Cite this chapter

Soekadar, S.R., Silvoni, S., Cohen, L.G., Birbaumer, N. (2015). Brain–Machine Interfaces in Stroke Neurorehabilitation. In: Kansaku, K., Cohen, L., Birbaumer, N. (eds) Clinical Systems Neuroscience. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55037-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-55037-2_1

  • Published:

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-55036-5

  • Online ISBN: 978-4-431-55037-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation