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Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton

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Abstract

The neurophysiological prerequisites for the development and operation of the brain-computer interfaces (BCI) that allow cerebral electrical signals alone to control external technical devices are considered. A BCI based on the discrimination of the EEG patterns related to imagery of extremity movements is described. The possibility of the rehabilitation of patients with motor disorders by means of the BCI based on motor imagery and the exoskeleton controlled by it is discussed.

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References

  1. Nudo, R.J., Milliken, G.W., Jenkins, W.M., and Merzenich, M.M., Use-Dependent Alterations of Movement Representations in Primary Motor Cortex of Adult Squirrel Monkeys, J. Neurosci., 1996, vol. 16, no. 2, p. 785.

    PubMed  CAS  Google Scholar 

  2. Bach-Y-Rita, P., Theoretical and Practical Considerations in the Restoration of Function after Stroke, Top Stroke Rehabilitation, 2001, vol. 8, p. 1.

    CAS  Google Scholar 

  3. Taub, E. Uswatte, G., and Elbert, T., New Treatments in Neurorehabilitation Founded on Basic Research, Nat. Rev. Neurosci., 2002, vol. 3, no. 3, p. 228.

    Article  PubMed  CAS  Google Scholar 

  4. Kwakkel, G., Wagenaar, R.C., Twisk, J.W., et al., Intensity of Leg and Arm Training after Primary Middle-Cerebral-Artery Stroke: A Randomized Trial, Lancet, 1999, vol. 354, p. 191.

    Article  PubMed  CAS  Google Scholar 

  5. Marchal-Crespo, L. and Reinkensmeyer, D.J., Review of Control Strategies for Robotic Movement Training after Neurologic Injury, J. Neuroengineering and Rehabilitation, 2009, vol. 6, no. 20, http://www.jneuroengrehab.com/content/6/1/20.

    Google Scholar 

  6. Reinkensmeyer, D.J., Kahn, L.E., Averbuch, M., et al., Understanding and Treating Arm Movement Impairment after Chronic Brain Injury: Progress with the ARM Guide, J. Rehabil. Res. Develop., 2000, vol. 37, no. 6, p. 653.

    CAS  Google Scholar 

  7. Lum, P.S., Burgar, C.G., Shor, P.C., et al., Robot-Assisted Movement Training Compared with Conventional Therapy Techniques for the Rehabilitation of the Upper-Limb Motor Function after Stroke, Arch. Phys. Med. Rehabil., 2002, vol. 83, no. 7, p. 952.

    Article  PubMed  Google Scholar 

  8. Hingtgen, B., McGuire, J.R., Wang, M., and Harris, G.F., An Upper Extremity Kinematic Model for Evaluation of Hemiparetic Stroke, J. Biomechanics, 2006, vol. 39, no. 4, p. 681.

    Article  Google Scholar 

  9. Masiero, S., Celia, A., Rosati, G., and Armani, M., Robotic-Assisted Rehabilitation of the Upper Limb after Acute Stroke, Arch. Phys. Med. Rehabil., 2007, vol. 88, no. 2, p. 142.

    Article  PubMed  Google Scholar 

  10. Amirabdollahian, F., Loureiro, R., Gradwell, E., et al., Multivariate Analysis of the Fugl-Meyer Outcome Measures Assessing the Effectiveness of the GENTLE/S Robot-Mediated Stroke Therapy, J. Neuroeng. Rehabil., 2007, vol. 4, p.4. doi 10.1186/1743-0003-4-4.

  11. Iwamuro, B.T. Cruz, E.G., Connelly, L.L. et al., Effect of Gravity-Compensating Orthosis on Reaching after Stroke: Evaluation of the Therapy Assistant WREX, Arch. Phys. Med. Rehabil., 2008, vol. 89, p. 2121.

    Article  PubMed  Google Scholar 

  12. Ferris, D.P., The Exoskeletons Are Here, J. Neuroeng. Rehabil., 2009, vol. 6, no. 17. http://www.jneuroengrehab.com/content/6/1/17.

    Google Scholar 

  13. Vidal, J.J., Towards Direct Brain-Computer Communication, Annu. Rev. Biophys. Bioeng., 1973, vol. 2, p. 157.

    Article  PubMed  CAS  Google Scholar 

  14. Vidal, J.J., Real-Time Detection of Brain Events in EEG, IEEE Proc. Special Issue on Biological Signal Processing and Analysis, 1977, vol. 65, p. 633.

    Google Scholar 

  15. Konrad, P. and Shanks, T., Implantable Brain Computer Interface: Challenges to Neurotechnology Translation, Neurobiol. Disord., 2010, vol. 38, p. 369.

    Article  Google Scholar 

  16. Wolpaw, J.R., Brain-Computer Interfaces as New Brain Output Pathways, J. Physiol., 2007, vol. 579, p. 613.

    Article  PubMed  CAS  Google Scholar 

  17. Pfurtscheller, G., EEG Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS), in Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Niedermeyer, E. and Lopes da Silva, F.H., Eds., Baltimore: Williams and Wilkins, 1999, 4th edition.

    Google Scholar 

  18. Pfurtscheller, G. and Berghold, A., Patterns of Cortical Activation during Planning of Voluntary Movement, EEG Clin. Neurophysiol., 1989, vol. 72, p. 250.

    Article  CAS  Google Scholar 

  19. Pfurtscheller, G. and Lopes da Silva, F.H., Event-Related EEG/MEG Synchronization and Desynchronization: Basic Principles, Clin. Neurophysol., 1999, vol. 110, p. 1842.

    Article  CAS  Google Scholar 

  20. Ivanitskii, G.A., Recognition of the Type of Mentally Solved Problem by Several EEG Seconds by Means of a Teachable Classifier, Zh. Vyssh. Nerv. Deyatel., 1997, vol. 47, p. 743.

    CAS  Google Scholar 

  21. Ivanitskii, G.A., Nikolaev, A.R., and Ivanitskii, A.M., Application of Artificial Neural Networks for Recognition of the Type of Intellectual Operations by EEG, Zh. Aviakosmich. Ekol. Med., 1993, vol. 31, p. 23.

    Google Scholar 

  22. Nikolaev, A.R., Ivanitskii, G.A., and Ivanitskii, A.M., Reproducible Patterns of the EEG Alpha-Rhythm in Solving Psychological Problems, Hum. Physiol., 1998, vol. 24, no. 3, p. 5.

    CAS  Google Scholar 

  23. Wolpaw, J.R. and McFarland, D.J., Control of Two-Dimensional Movement Signal by a Noninvasive Brain-Computer Interface in Humans, Proc. Natl. Acad. Sci. U.S.A., 2004, vol. 101, no. 51, p. 17849.

    Article  PubMed  CAS  Google Scholar 

  24. Georgopoulos, A.P., Kalaska, J.F., Crutcher, M.D., et al., On the Relations between the Direction of Two-Dimensional (2D) Arm Movements and Cell Discharge in Primate Motor Cortex, J. Neurosci., 1982, vol. 2, p. 1527.

    PubMed  CAS  Google Scholar 

  25. Lebedev, M.A. and Nicoletis, M.A.L., Brain-Machine Interfaces: Past, Present, and Future, Trends in Neurisci., 2006, vol. 29, no. 9, p. 536.

    Article  CAS  Google Scholar 

  26. Sutton, S., Braren, M., Zubin, J., and John, E.R., Evoked Correlates of Stimulus Uncertainty, Science, 1965, vol. 150, p. 1187.

    Article  PubMed  CAS  Google Scholar 

  27. Donchin, E. and Smith, D.B., The Contingent Negative Variation and the Late Positive Wave of the Average Evoked Potential, EEG and Clin. Neurophysiol., 1970, vol. 29, p. 201.

    Article  CAS  Google Scholar 

  28. Farwell, L.A. and Donchin, E., Talking Off the Top of Your Head: A Mental Prosthesis Utilizing Event-Related Brain Potentials, EEG and Clin. Neurophysiol., 1988, vol. 70, p. 510.

    Article  CAS  Google Scholar 

  29. Donchin, E., Spencer, K.M., and Wijesinghe, R., The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface, IEEE Trans Rehabil, 2000, vol. 8, no. 2, p. 174.

    Article  CAS  Google Scholar 

  30. Millan, J. del, R., Mourino, J., Marciani, M.G., et al., Adaptive Brain Interfaces for Physically Disabled People, 2nd Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Science, Hong Kong, 1998, p. 2008.

    Google Scholar 

  31. Bobrov, P.D., Husek, D., Korshakov, A.V., and Frolov, A.A., Sources of Brain Activity Significant and Insignificant for Classification of the EEG Patterns Corresponding to Motor Imagery, Neirokomp. Razrab. Prim., 2011, no. 12, p. 1.

    Google Scholar 

  32. Bobrov, P.D., Korshakov, A.V., Roshchin, V.Yu., and Frolov, A.A., Bayesian Approach to Realization of the Brain-Computer Interface Based on Motor Imagery, Zh. Vyssh. Nerv. Deyatel., 2012, vol. 62, p. 89.

    CAS  Google Scholar 

  33. Frolov, A., Husek, D., Bobrov, P., Comparison of Four Classification Methods for Brain-Computer Interface, Neural Network World, 2011, vol. 21, no. 2, p. 101.

    Google Scholar 

  34. Bobrov, P., Frolov, A., Cantor, C., et al., Brain-Computer Interface Based on Generation of Visual Images, PloS ONE, 2011, vol. 6, no. 6, p. e20674, doi: 10.1371/journal.pone.0020674.

    Article  PubMed  CAS  Google Scholar 

  35. Frolov, A., Husek, D., Bobrov, P., et al., Sources of EEG Activity the Most Relevant to Performance of Brain-Computer Interface Based on Motor Imagery, Neural Network World, 2012, vol. 22, no. 1, p. 21.

    Google Scholar 

  36. Jeannerod, M., Neural Simulation of Action: A Unifying Mechanism for Motor Cognition, Neuroimage, 2001, vol. 14, p. 103.

    Article  Google Scholar 

  37. Jeannerod, M. and Frak, V., Mental Imaging of Motor Activity in Humans, Curr. Opin. Neurobiol., 1999, vol. 9, p. 735.

    Article  PubMed  CAS  Google Scholar 

  38. Neuper, C., Scherer, R., Reiner, M., and Pfurtscheller, G., Imagery of Motor Actions: Differential Effects of Kinesthetic and Visual-Motor Mode of Imagery in Single-Trial EEG, Cogn. Brain Res., 2005, vol. 25, no. 3, p. 668.

    Article  Google Scholar 

  39. Bloom, H.J.G., Lazerson, A., and Nelson, C.A., Brain, Mind, and Behavior, Worth Publishers, 2000, 3rd edition.

    Google Scholar 

  40. Kohavi, R. and Provost, F., Glossary of Terms. Machine Learning, Special Issue of Appl. Machine Learn. Knowledge Disc. Proc., 1998, vol. 30, p. 271.

    Google Scholar 

  41. Kachenoura, A., Albera, L., Senhadji, L., and Comon, P., ICA: A Potential Tool for BCI Systems, IEEE Signal Processing Magazine, 2008, vol. 25, no. 1, p. 57.

    Article  Google Scholar 

  42. Delorme, A. and Makeig, S., EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics, J. Neurosci. Methods, 2004, vol. 134, p. 9.

    Article  PubMed  Google Scholar 

  43. Hyvarinen, A., Karhunen, J., and Oje, E., Independent Component Analysis, New York: Willey, 2001.

    Book  Google Scholar 

  44. Rougeul-Buser, A. and Buser, P., Rhythms in the Alpha Band in Cats and Their Behavioral Correlates, Int. J. Psychophysiol., 1997, vol. 26, p. 191.

    Article  PubMed  CAS  Google Scholar 

  45. Frolov, A.A., Roshchin, V.Y., and Buryukova, E.V., Adaptive Neural Network Model of Multijoint Movement Control by Working Point Velocity, Neural Network World, 1994, vol. 4, no. 2, p. 141.

    Google Scholar 

  46. Grinyagin, I.V., Biryukova, E.V., and Maier, M.A., Kinematic and Dynamic Synergies of Human Precision Grip Movement, J. Neurophysiol., 2005, vol. 94, p. 2284.

    Article  PubMed  CAS  Google Scholar 

  47. Fel’dman, A.G., Tsentral’nye i reflektornye mekhanizmy upravleniya dvizheniem (Central and Reflex Mechanisms of Movement Control), Moscow: Nauka, 1979.

    Google Scholar 

  48. Bizzi, E., Acconero, N., Chapple, W., and Hogan, N., Arm Trajectory Formation, Exp. Brain Res., 1982, vol. 46, p. 139.

    Article  PubMed  CAS  Google Scholar 

  49. Gomi, H. and Kawato, M., Equilibrium-Point Control Hypothesis Examined by Measured Arm Stiffness during Multijoint Movement, Science, 1996, vol. 272, p. 117.

    Article  PubMed  CAS  Google Scholar 

  50. Peterka, R.J., Sensorimotor Integration in Human Postural Control, J. Neurophysiol., 2002, vol. 88, p. 1097.

    PubMed  CAS  Google Scholar 

  51. Frolov, A.A., Dufosse, M., Rizek, S., and Kaladjan, A., On the Possibility of Linear Modeling of the Human Arm Neuromuscular Apparatus, Biol. Cybern., 2000, vol. 82, no. 6, p. 499.

    Article  PubMed  CAS  Google Scholar 

  52. Frolov, A.A., Prokopenko, R.A., Dufosse, M., and Ouezdou, F.B., Adjustment of the Human Arm Viscoelastic Properties to the Direction of Reaching, Biol. Cybern., 2006, vol. 94, p. 97.

    Article  PubMed  CAS  Google Scholar 

  53. Alexandrov, A.V., Frolov, A.A., Horak, F.B., et al., Feedback Equilibrium Control during Human Standing, Biol. Cybern., 2005, vol. 93, p. 309.

    Article  PubMed  CAS  Google Scholar 

  54. Bernshtein, N.A., O lovkosti i ee razvitii (About Dexterity and Its Development), Moscow: Fizkul’tura i Sport, 1947/1991.

    Google Scholar 

  55. Gomi, H. and Kawato, M., Human Arm Stiffness and Equilibrium-Point Trajectory during Multijoint Movement, Biol. Cybern., 1998, vol. 76, p. 163.

    Article  Google Scholar 

  56. Hingtgen, B., McGuire, J.R., Wang, M., and Harris, G.F., An Upper Extremity Kinematic Model for Evaluation of Hemiparetic Stroke, J. Biomechanics, 2006, vol. 39, no. 4, p. 681.

    Article  Google Scholar 

  57. An, K.N., Jacobsen, M.C., Berglund, L.J., and Chao, E.Y.S., Application of a Magnetic Tracking Device to Kinesiologic Studies, J. Biomechanics, 1988, vol. 21, p. 613.

    Article  CAS  Google Scholar 

  58. Biryukova, E.V., Roby-Brami, A., Frolov, A.A., and Mokhtari, M., Kinematics of Human Arm Reconstructed from Spatial Tracking System Recordings, J. Biomechanics, 2000, vol. 33, no. 8, p. 985.

    Article  CAS  Google Scholar 

  59. Prokopenko, R.A., Frolov, A.A., Biryukova, E.V., and Roby-Brami, A., Assessment of the Accuracy of a Human Arm Model with Seven Degrees of Freedom, J. Biomechanics, 2001, vol. 34, no. 2, p. 177.

    Article  CAS  Google Scholar 

  60. Korenev, G.V., Tselenapravlennaya mekhanika upravlyaemykh manipulyatorov (Purpose-Oriented Mechanics of Controlled Manipulators), Moscow: Nauka, 1979.

    Google Scholar 

  61. Winter, D.A., Biomechanics and Motor Control in Human Movement, New York: John Wiley and Sons, 1990, 2nd edition.

    Google Scholar 

  62. Biryukova, E.V., Roshchin, V.Y., Frolov, A.A., et al., Forearm Postural Control during Unloading: Anticipatory Changes in Elbow Stiffness, Exp. Brain Res., 1999, vol. 124, no. 1, p. 107.

    Article  PubMed  CAS  Google Scholar 

  63. Alexandrov, A.V. and Frolov, A.A., Closed-Loop and Open-Loop Control of Posture and Movement during Human Upper Trunk Bending, Biol. Cybern., 2011, vol. 104, no. 6, p. 425.

    Article  PubMed  CAS  Google Scholar 

  64. Gur’ev, V.V., Zorya, V.I., Biryukova, E.V., et al., Biomechanical Analysis of the Indicators of Movement at the Lower Extremity Joints in Patients with Coxarthrosis as a Method of Functional Diagnostics, Vestn. Eksp. Klin. Khirurg., 2011, vol. 4, no. 1, p. 94.

    Google Scholar 

  65. Alexandrov, A.V. and Frolov, A.A., Organization of Direct Motor Control during Human Upper Trunk Bending, Ross. Zh. Biomekh., 2010, vol. 14, p. 19.

    Google Scholar 

  66. Hettich, G., Mergner, T., Gollhofer, A., Weiller, C., Alexandrov, A.V., and Frolov, A.A. Human-Inspired Robot as a Platform for Comparing between Human Stance Control Models, BC11: Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011 (Proceedings of the Conference), 2011, p. 21–23.

    Google Scholar 

  67. Alexandrov, A.V., Frolov, A.A., and Massion, J., Biomechanical Analysis of Movement Strategies in Human Forward Trunk Bending, I. Modeling, Biol. Cybern., 2001, vol. 84, p. 425.

    Article  PubMed  CAS  Google Scholar 

  68. Gel’fand, I.M. and Tsetlin, M.L., About Mathematical Modeling of the Mechanisms of the Central Nervous System, in Modeli strukturno-funktsional’noi organizatsii nekotorykh biologicheskikh sistem (Models of Structural-Functional Organization of Certain Biological Systems), Gel’fand, I.M., Gurfinkel’, V.S., Fomin, S.V., and Tsetlin, M.L., Eds., Moscow: Nauka, 1966.

    Google Scholar 

  69. Weiss, P., Self-Differentiation of Basic Patterns of Coordination, Comparative Psychological Monograph, 1961, vol. 17, p. 1.

    Google Scholar 

  70. Netz, J., Lammers, T., and Homberg, V., Reorganization of Motor Output in the Nonaffected Hemisphere after Stroke, Brain, 1997, vol. 120, p. 1579.

    Article  PubMed  Google Scholar 

  71. Platz, T., Kim, I.H., Pintschovius, H., et al., Multimodal EEG Analysis in Man Suggests Impairment-Specific Changes in Movement-Related Electric Brain Activity after Stroke, Brain, 2000, vol. 123, p. 2475.

    Article  PubMed  Google Scholar 

  72. Cramer, S.C., Functional Imaging in Stroke Recovery, Stroke, 2004, vol. 35, p. 2695.

    Article  PubMed  Google Scholar 

  73. Ward, N.S. and Cohen, L.G., Mechanisms Underlying Recovery of Motor Function after Stroke, Arch. Neurol., 2004, vol. 61, p. 1844.

    Article  PubMed  Google Scholar 

  74. Jack, D., Boian, R., Merians, A.S., et al., Virtual Reality-Enhanced Stroke Rehabilitation, IEEE Trans. Neural Syst. Rehabil. Eng., 2001, vol. 9, p. 308.

    Article  PubMed  CAS  Google Scholar 

  75. Holden M.K., Dyar, T.A., Schwamm, L., and Bizzi, E., Virtual Environment-Based Telerehabilitation in Patients with Stroke, Presence Teleoperat. Virtual Environ., 2005, vol. 14, p. 214.

    Article  Google Scholar 

  76. Takahashi, C.D., Der-Yeghianian, L., Le, V., et al., Robot-Based Hand Motor Therapy after Stroke, Brain, 2008, vol. 131, p. 425.

    Article  PubMed  Google Scholar 

  77. Volpe, B.T., Huerta, P.T., Zipse, J.L., et al., Robotic Devices as Therapeutic and Diagnostic Tools for Stroke Recovery, Arch. Neurol., 2009, vol. 66, p. 1086.

    Article  PubMed  Google Scholar 

  78. Decety, J. and Jeannerod, M., Mentally Stimulated Movements in Virtual Reality: Does Fitt’s Law Hold in Motor Imagery?, Behav. Brain Res., 1995, vol. 72, p. 127.

    Article  PubMed  CAS  Google Scholar 

  79. Berthoz, A., The Role of Inhibition in the Hierarchical Gating of Executed and Imagined Movements, Cogn. Brain Res., 1996, vol. 3, p. 101.

    Article  CAS  Google Scholar 

  80. Lotze, M. and Halsband, U., Motor Imagery, J. Physiol., 2006, vol. 99, p. 386.

    Google Scholar 

  81. Daly, J.J. and Wolpaw, J.R., Brain-Computer Interfaces in Neurological Rehabilitation, Lancet Neurol., 2008, vol. 7, no. 11, p. 1032.

    Article  PubMed  Google Scholar 

  82. Birbaumer, N. and Cohen, L.G., Brain-Computer Interfaces: Communication and Restoration of Movement in Paralysis, J. Physiol. Special Issue on Brain-Computer Interfaces, 2007, vol. 579, p. 621.

    CAS  Google Scholar 

  83. Buch, E., Weber, C., Cohen, L.G., et al., Think to Move: A Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke, Stroke, 2008, vol. 39, p. 910.

    Article  PubMed  Google Scholar 

  84. Daly, J.J., Fang, Y., Perepezko, E.M., et al., Prolonged Cognitive Planning Time, Elevated Cognitive Effort, and Relationship to Coordination and Motor Control following Stroke, IEEE Trans. Neural Syst. Rehabil. Eng., 2006, vol. 14, p. 168.

    Article  PubMed  Google Scholar 

  85. Nudo, R.J., Wise, B.M., Sifuentes, F., and Milliken, G.W., Neural Substrates for the Effects of Rehabilitative Training on Motor Recovery after Ischemic Infarct, Science, 1996, vol. 272, p. 1791.

    Article  PubMed  CAS  Google Scholar 

  86. Rizzolatti, G., Fogassi, L., and Galesse, V., Neurophysiological Mechanisms Underlying the Understanding and Imitation of Action, Nat. Rev. Neurosci., 2001, vol. 2, p. 661.

    Article  PubMed  CAS  Google Scholar 

  87. Iacoboni, M., Koski, L.M., and Brass, M., Reafferent Copies of Imitated Actions in the Right Superior Temporal Cortex, Proc. Natl. Acad. Sci. U.S.A., 2001, vol. 98, p. 13995.

    Article  PubMed  CAS  Google Scholar 

  88. Carmichael, S.T. and Chesselet, M.F., Synchronous Neuronal Activity Is a Signal for Axonal Sprouting after Cortical Lesions in the Adult, J. Neurosci., 2002, vol. 22, p. 6062.

    PubMed  CAS  Google Scholar 

  89. Ring, H. and Rosenthal, N., Controlled Study of Neuroprosthetic Functional Electrical Stimulation in Subacute Poststroke Rehabilitation, J. Rehabil. Med., 2005, vol. 37, p. 32.

    Article  PubMed  Google Scholar 

  90. Alon, G., Sunnerhagen, K.S., Geurts, A.C., and Ohry, A., A Home-Based, Self-Administered Stimulation Program to Improve Selected Hand Functions of Chronic Stroke, Neuro Rehabil., 2003, vol. 18, p. 215.

    Google Scholar 

  91. Daly, J.J., Hogan, N., and Perepezko, E.M., Response to Upper-Limb Robotics and Functional Neuromuscular Simulation following Stroke, J. Rehabil. Res. Dev., 2005, vol. 42, p. 723.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia

    A. A. Frolov, E. V. Biryukova, P. D. Bobrov & O. A. Mokienko

  2. Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, Russia

    A. K. Platonov & V. E. Pryanichnikov

  3. Sensorika International Laboratory, Moscow, Russia

    V. E. Pryanichnikov & L. A. Chernikova

  4. Ostrava Technical University, Ostrava, Czech Republic

    A. A. Frolov & P. D. Bobrov

  5. Research Center of Neurology, Russian Academy of Medical Sciences, Moscow, Russia

    O. A. Mokienko

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  1. A. A. Frolov
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Original Russian Text © A.A. Frolov, E.V. Biryukova, P.D. Bobrov, O.A. Mokienko, A.K. Platonov, V.E. Pryanichnikov, L.A. Chernikova, 2013, published in Fiziologiya Cheloveka, 2013, Vol. 39, No. 2, pp. 99–113.

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Frolov, A.A., Biryukova, E.V., Bobrov, P.D. et al. Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton. Hum Physiol 39, 196–208 (2013). https://doi.org/10.1134/S0362119713020035

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