Brain-computer interface as a novel tool of neurorehabilitation

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Abstract

Brain-computer interfaces (BCIs) are invasive or non-invasive technologies allowing brain signals to be translated into commands of the external devices. Nowadays this technology is actively developing for the use in rehabilitation of patients with neurological diseases. Such interfaces can serve as a means of interaction with the outside world for patients with the «lockedin » syndrome. Using BCI patients with movement disorders could control robotic prostheses, wheelchairs and other external technical devices. Interfaces with biofeedback can facilitate the reorganization of the damaged cortex. Patients with neurological disorders were found to be able to use brain-computer interface. Nevertheless, it is necessary to perform larger controlled clinical studies for the evaluation of BCI effectiveness in neurorehabilitation.

 

About the authors

O. A. Mokienko

Insitute of Higher Nervous Activity and Neurophysiology of RAS

Author for correspondence.
Email: platonova@neurology.ru
Russian Federation

L. A. Chernikova

Insitute of Higher Nervous Activity and Neurophysiology of RAS

Email: platonova@neurology.ru
Russian Federation

A. A. Frolov

Insitute of Higher Nervous Activity and Neurophysiology of RAS

Email: platonova@neurology.ru
Russian Federation

References

  1. Ang K.K., Guan C., Chua K.S. et al. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010; 2010: 5549–5552.
  2. Ang K.K., Guan C., Chua K.S. et al. A clinical evaluation of noninvasive motor imagery-based brain-computer interface in stroke. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008; 2008: 4178–4181.
  3. Ball T., Kern M., Mutschler I. et al. Signal quality of simultaneously recorded invasive and non-invasive EEG. Neuroimage. 2009 Jul. 1; 46 (3): 708–716.
  4. Birbaumer N., Ghanayim N., Hinterberger T. et al. A spelling device for the paralysed. Nature 1999 Mar. 25; 398 (6725): 297–298.
  5. Birbaumer N., Hinterberger T., Kubler A., Neumann N. The thoughttranslation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans. Neural Syst. Rehabil. Eng. 2003 Jun.; 11 (2): 120–123.
  6. Birbaumer N., Ramos Murguialday A., Weber C., Montoya P. Neurofeedback and brain-computer interface clinical applications. Int. Rev. Neurobiol. 2009; 86: 107–117.
  7. Blankertz B., Dornhege G., Krauledat M. et al. The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. Neuroimage 2007 Aug. 15; 37 (2): 539–550.
  8. Bradberry T.J., Gentili R.J., Contreras-Vidal J.L. Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. J. Neurosci. 2010 Mar. 3; 30 (9): 3432–3437.
  9. Broetz D., Braun C., Weber C. et al. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil. Neural. Repair. 2010 Sep.; 24 (7): 674–679.
  10. Buch E., Weber C., Cohen L.G. et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 2008 Mar.; 39 (3): 910–917.
  11. Calautti C., Naccarato M., Jones P.S. et al. The relationship between motor deficit and hemisphere activation balance after stroke: A 3T fMRI study. Neuroimage 2007 Jan. 1; 34 (1): 322–331.
  12. Caria A., Veit R., Sitaram R. et al. Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage 2007 Apr. 15; 35 (3): 1238–1246.
  13. Caria A., Weber C., Brotz D. et al. Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology 2011 Apr.; 48 (4): 578–582.
  14. Carmena J.M., Lebedev M.A., Crist R.E. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 2003 Nov.; 1 (2): E42.
  15. Daly J.J., Cheng R., Rogers J. et al. 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. 2009 Dec.; 33 (4): 203–211.
  16. deCharms R.C., Maeda F., Glover G.H. et al. Control over brain activation and pain learned by using real-time functional MRI. Proc. Natl. Acad. Sci. USA 2005 Dec. 20; 102 (51): 18626–18631.
  17. 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. 2007 Mar. 15; 579 (3): 603–611.
  18. Fabiano G.A., Chacko A., Pelham W.E. et al. A comparison of behavioral parent training programs for fathers of children with attentiondeficit/ hyperactivity disorder. Behav. Ther. 2009 Jun.; 40 (2): 190–204.
  19. Farwell L.A., Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 1988 Dec.; 70 (6): 510–523.
  20. Freeman W.J., Rogers L.J., Holmes M.D., Silbergeld D.L. Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands. J. Neurosci. Methods. 2000 Feb. 15; 95 (2): 111–121.
  21. Gastaut H., Terzian H., Gastaut Y. [Study of a little electroencephalographic activity: rolandic arched rhythm]. Mars Med. 1952; 89 (6): 296–310.
  22. Hochberg L.R., Serruya M.D., Friehs G.M. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006 Jul. 13; 442 (7099): 164–171.
  23. Kennedy P.R., Bakay R.A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport. 1998 Jun. 1; 9 (8): 1707–1711.
  24. Kotchoubey B., Strehl U., Uhlmann C. et al. Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 2001 Mar.; 42 (3): 406–416.
  25. Kubler A., Kotchoubey B., Kaiser J. et al. Brain-computer communication: unlocking the locked in. Psychol. Bull. 2001 May; 127 (3): 358–375.
  26. Kubler A., Nijboer F., Mellinger J. et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005 May 24; 64 (10): 1775–1777.
  27. Lebedev M.A., Nicolelis M.A. Brain-machine interfaces: past, present and future. Trends Neurosci. 2006 Sep.; 29 (9): 536–546.
  28. Lenhardt A., Kaper M., Ritter H.J. An adaptive P300-based online brain-computer interface. IEEE Trans Neural. Syst. Rehabil. Eng. 2008 Apr.; 16 (2): 121–130
  29. . 29. Leuthardt E.C., Schalk G., Wolpaw J.R. et al. A brain-computer interface using electrocorticographic signals in humans. J. Neural. Eng. 2004 Jun.; 1 (2): 63–71. 30. Logothetis N.K., Pauls J., Augath M. et al. Neurophysiological investigation of the basis of the fMRI signal. Nature 2001 Jul. 12; 412 (6843): 150–157. 31. Lotze M., Grodd W., Birbaumer N. et al. Does use of a myoelectric prosthesis prevent cortical reorganization and phantom limb pain? Nat. Neurosci. 1999 Jun.; 2 (6): 501–502.
  30. McFarland D.J., Krusienski D.J., Sarnacki W.A. et al. Emulation of computer mouse control with a noninvasive brain-computer interface. J. Neural. Eng. 2008 Jun.; 5 (2): 101–110.
  31. McFarland D.J., Miner L.A., Vaughan T.M., Wolpaw J.R. Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr. 2000 Spring; 12 (3): 177–186.
  32. Mellinger J., Schalk G., Braun C. et al. An MEG-based brain-computer interface (BCI). Neuroimage. 2007 Jul. 1; 36 (3): 581–593.
  33. Meng F., Tong K-yR., Chan S-tP. et al. BCI-FES training system design and implementation for rehabilitation of stroke patients. Proceedings of the International Joint Conference on Neural Networks; June; Hong Kong, China: IEEE World Congress on Computational Intelligence; 2008: 4103–4106.
  34. Mohapp A., Scherer R., Keinrath C. et al. Single-trial EEG classification of executed and imagined hand movements in hemiparetic stroke patients. 3rd International BCI Workshop and Training Course; Graz 2006: 80–81.
  35. Nagaoka T., Sakatani K., Awano T. et al. Development of a new rehabilitation system based on a brain-computer interface using nearinfrared spectroscopy. Adv. Exp. Med. Biol. 2010; 662: 497–503.
  36. Perelmouter J., Birbaumer N. A binary spelling interface with random errors. IEEE Trans Rehabil. Eng. 2000 Jun.; 8(2): 227–232.
  37. Pfurtscheller G., Aranibar A. Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr. Clin. Neurophysiol. 1979 Feb.; 46 (2): 138–146.
  38. Pfurtscheller G., Graimann B., Huggins J.E., Levine S.P. Brain-computer communication based on the dynamics of brain oscillations. (Suppl.: Clin Neurophysiol.) 2004; 57: 583–591.
  39. Pfurtscheller G., Guger C., Muller G. et al.Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 2000 Oct. 13; 292 (3): 211–214.
  40. Pfurtscheller G., Muller G.R., Pfurtscheller J. et al. ‘Thought’—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 2003 Nov. 6; 351 (1): 33–36.
  41. Platz T., Kim I.H., Engel U. et al. 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. 2002; 20 (1–2): 21–35.
  42. Prasad G., Herman P., Coyle D. et al. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J. Neuroeng. Rehabil. 2010; 7 (1): 60.
  43. Rockstroh B., Birbaumer N., Elbert T., Lutzenberger W. Operant control of EEG and event-related and slow brain potentials. Biofeedback Self Regul. 1984 Jun.; 9 (2): 139–160.
  44. Seifert A.R., Lubar J.F. Reduction of epileptic seizures through EEG biofeedback training. Biol Psychol. 1975 Nov.; 3 (3): 157–184. 47. Serruya M.D., Hatsopoulos N.G., Paninski L., Fellows M.R. et al. Instant neural control of a movement signal. Nature 2002 Mar. 14; 416 (6877): 141–142.
  45. Sitaram R., Caria A., Birbaumer N. Hemodynamic brain-computer interfaces for communication and rehabilitation. Neural. Netw. 2009 Nov.; 22 (9): 1320–1328.
  46. Staba R.J., Wilson C.L., Bragin A., Fried I., Engel J., Jr. Quantitative analysis of high-frequency oscillations (80-500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. J. Neurophysiol. 2002 Oct.; 88 (4): 1743–1752.
  47. Strehl U., Leins U., Goth G., Klinger C., Hinterberger T., Birbaumer N. Self-regulation of slow cortical potentials: a new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics 2006 Nov.;118 (5): 1530–1540.
  48. Taylor D.M., Tillery S.I., Schwartz A.B. Direct cortical control of 3D neuroprosthetic devices. Science 2002 Jun. 7; 296 (5574): 1829–1832.
  49. Velliste M., Perel S., Spalding M.C. et al. Cortical control of a prosthetic arm for self-feeding. Nature 2008 Jun. 19; 453 (7198): 1098–1101.
  50. Vidal J.J. Toward direct brain-computer communication. Annu Rev. Biophys. Bioeng. 1973; 2: 157–180. 54. Waldert S., Preissl H., Demandt E. et al. Hand movement direction decoded from MEG and EEG. J. Neurosci. 2008 Jan. 23; 28 (4): 1000–1008.
  51. Ward N.S., Cohen L.G. Mechanisms underlying recovery of motor function after stroke. Arch. Neurol 2004 Dec.; 61 (12): 1844–1848.
  52. Weiskopf N., Veit R., Erb M. et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 2003 Jul.; 19 (3): 577–586.
  53. Wolpaw J.R. Brain-computer interfaces as new brain output pathways. J. Physiol. 2007 Mar. 15; 579 (Pt 3): 613–619.
  54. Wolpaw J.R., Birbaumer N., McFarland D.J. et al. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 2002 Jun.; 113 (6): 767–791.
  55. 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 2004 Dec. 21; 101 (51): 17849–17854.
  56. Yoo S.S., Fairneny T., Chen N.K. et al. Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport. 2004 Jul. 19; 15 (10): 1591–1595.

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Copyright (c) 2017 Mokienko O.A., Chernikova L.A., Frolov A.A.

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