This review addresses the use of robotic devices in the rehabilitation of poststroke and posttrauma patients as a rehabilitation technology which has developed rapidly over the last decade. The types of devices used are described – manipulators and exoskeletons – along with the clinical protocols for their use and the effectiveness of rehabilitation procedures. Particular attention is paid to the neurophysiological basis of the rehabilitation potential of this technology, including analysis of measures of plastic rearrangements of the brain. Results obtained from state-of-the-art rehabilitation technology using hand exoskeletons controlled by brain–computer interfaces based on kinesthetic motor imagery are considered.
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Alexandrov, A. V. and Frolov, A. A., “Closed-loop and open-loop control of posture and movement during human upper trunk bending,” Biol. Cybern., 104, No. 6, 425–438 (2011).
Alexandrov, A. V., Frolov, A. A., Horak, F. B., Carlson-Kuhta, P., and Park, S., “Feedback equilibrium control during human standing,” Biol. Cybern., 93, 309–322 (2005).
Alt Murphy, M. A. and Häger, C. K., “Kinematic analysis of the upper extremity alter stroke – how far have we reached and what have we grasped?” Phys. Ther. Rev., 20, 137–155 (2015).
Aman, K. and Al-Jumailyb, A. A., “Active exoskeleton control systems: state of the art,” Procedia Engineering, 41, 988–994 (2012).
Amirabdollahian, F., Loureiro, R., Gradwell, E., Collin, C., Harwin, W., and Johnson, G., “Multivariate analysis of the Fugl-Meyer outcome measures assessing the effectiveness of GENTLE/S robot-mediated stroke therapy,” J. Neuroeng. Rehabil., 4, No. 1, 4 (2007).
Ang, K. K., Chua, K. S., Phua, K. S., Wang, C., Chin, Z. Y., Kuah, C. W., Low, W., and Guan, C., “A randomized controlled trial of EEGbased motor imagery brain–computer interface robotic rehabilitation for stroke,” Clin. EEG Neurosci., 46, No. 4, 310–320 (2015).
Ang, K. K., Guan, C., Chua, K. S., and Ang, B. T., Kuah, C. W., Wang, C., Phua, K. S., Chin, Z. Y., and, Zhang, H., “A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain–computer interface,” Clin. EEG Neurosci., 42, No. 4, 253–258 (2011).
Ang, K. K., Guan, C., Phua, K. S., Wang, C., Zhou, L., Tang, K. Y., Ephraim Joseph, G. J., Kuah, C. W., “Brain–computer interfacebased robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke,” Front. Neuroeng., 7, 30 (2014).
Bach-Y-Rita, P., “Theoretical and practical considerations in the restoration of function after stroke,” Top. Stroke Rehabil., 8, No. 3, 1–15 (2001).
Balasubramanian, S., Wei, R., Perez, M., Shepard, B., Koeneman, E., Koeneman, J., and He, J., “RUPERT: An exoskeleton robot for assisting rehabilitation of arm functions,” Proceedings of the Conference Virtual Rehabilitation, Vancouver, Canada (2008), pp. 163–167.
Bernshtein, N. A., Dexterity and its Development, Fizkul’tura i Sport, Moscow (1947b).
Bernshtein, N. A., Studies of the Biodynamics of Locomotion, VIEM, Moscow, Leningrad (1935).
Bernshtein, N. A., The Construction of Movements, Meditsina, Moscow (1947a).
Biryukova, E. V., Pavlova, O. G., Kurganskaya, M. E., Bobrov, P. D., Turbina, L. G., Frolov, A. A., Davydov, V. I., Sil’chenko, A. V., and Mokienko, O. A., “Recovery of the motor function of the arm using a hand exoskeleton controlled by a ‘brain–computer’ interface. A case of a patient with extensive damage to brain structures,” Fiziol. Cheloveka, 42, No. 1, 19–30 (2016).
Biryukova, E. V., Roschin, V. Y., Frolov, A. A., Ioffe, M. E., Massion, J., and Dufosse, M., “Forearm postural control during unloading: anticipatory changes in elbow stiffness,” Exp. Brain Res., 124, No. 1, 107–117 (1999).
Bizzi, E., Acconero, N., Chapple, W., and Hogan, N., “Arm trajectory formation,” Exp. Brain Res., 46, 139–143 (1982).
Bobrov, P. D., Isaev, M. R., Korshakov, A. V., Oganesyan, V. V., Kerechanin, Ya. V., Popod’ko, A. I., and Frolov, A. A., “Sources of electrophysiological and foci of hemodynamic activity in the brain signifi -cant for the control of a hybrid brain–computer interface based on the recognition of EEG patterns and near infrared spectrograms on motor imagery,” Fiziol. Cheloveka, 42, No. 3, 12–24 (2016).
Bobrov, P., Frolov, A. A., and Húsek, D., “Brain computer interface enhancement by independent component analysis,” in: Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011, Springer, Berlin, Heidelberg (2011), pp. 51–60.
Bos, R. A., Haarman, C. J. W., Stortelder, T., Nizamis, K., Herder, J. L., Stienen, A. H. A., and Plettenburg, D. H., “A structured overview of trends and technologies used in dynamic hand orthoses,” J. Neuroeng. Rehabil., 13, 1 (2016).
Brauchle, D., Vukelic, M., Bauer, R., and Gharabaghi, A., “Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation,” Front. Hum. Neurosci., 9, 564 (2015).
Brewer, B. R., Klatzky, R., and Matsuoka, Y., “Visual feedback distortion in a robotic environment for hand rehabilitation,” Brain Res. Bull., 75, No. 6, 804–813 (2008).
Buch, E., Weber, C., Cohen, L. G., Braun, C., Dimyan, M. A., Ard, T., Mellinger, J., Caria, A., et al., “Think to move: a neuromagnetic brain–computer interface (BCI) system for chronic stroke,” Stroke, 39, 910–917 (2008).
Cordo, P., Lutsep, H., Cordo, L., Wright, W. G., Cacciatore, T., and Skoss, R., “Assisted movement with enhanced sensation (AMES): coupling motor and sensory to remediate motor defi cits in chronic stroke patients,” Neurorehabil. Neural Repair, 23, 67–77 (2009).
Delorme, A., Palmer, J., Onto, J., Oostenveld, R., and Makeig, S., “Independent EEG sources are dipolar,” PLoS One, 7, No. 2, e30135 (2012).
Di Pino, G., Pellegrino, G., Assenza, G., Capone, F., Ferreri, F., Formica, D., Ranieri, F., Tombini, M., “Modulation of brain plasticity in stroke: a novel model for neurorehabilitation,” Nat. Dev. Neurol., 10, 597–608 (2014).
Dipietro, L., Ferraro, M., Palazzolo, J. J., Krebs, H. I., Volpe, B. T., and Hogan, N., “Customized interactive robotic treatment for stroke: EMG triggered therapy,” IEEE Trans. Neur. Syst. Rehab. Eng., 13, No. 3, 325–334 (2005).
Dovat, L., Lambercy, O., Gassert, R., Maeder, T., Milner, T., Leong, T. C., and Burdet, E., “HandCARE: a cable-actuated rehabilitation system to train hand function after stroke,” IEEE Trans. Neural Syst. Rehabil. Eng., 16, No. 6, 582–591 (2008).
Fazekas, G., Horwath, M., and Toth, A., “A novel robot training system designed to supplement upper limb physiotherapy on patient with spastic hemiparesis,” Int. J. Rehabil. Res., 29, 251–254 (2006).
Fel’dman, A. G., Central and Refl ex Mechanisms of Motor Control, Nauka, Moscow (1979).
Ferris, D. P., “The exoskeletons are here,” J. Neuroeng. Rehabil., 6, 17 (2009).
Frisoli, A., Borelli, L., Montagner, A., Marcheschi, S., Procopio, C., Salsedo, F., Bergamasco, M., Carboncini, M. C., et al., “Arm rehabilitation with a robotic exoskeleton in virtual reality,” Proceedings of the IEEE 10th International Conference on Rehabilitation and Robotics (ICORR), Noordwijk, Netherlands (2007), pp. 631–642.
Frolov, A. A., Dufosse, M., Rizek, S., and Kaladjan, A., “On the possibility of linear modeling of the human arm neuromuscular apparatus,” Biol. Cybern., 82, No. 6, 499–515 (2000).
Frolov, A. A., Gusek, D., Sil’chenko, A. V., Tintera, Ya., and Rydlo, Ya., “Changes in the hemodynamic activity of the brain in motor imagery as a result of training subjects to control a brain–computer interface,” Fiziol. Cheloveka, 42, No. 1, 5–18 (2016a).
Frolov, A. A., Husek, D., Biryukova, E., V., Bobrov, P. D., Mokienko, O. A., and Alexandrov, A. V., “Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain–computer interface based on motor imagery,” Neural Network World, 27, No. 1, 107–137 (2017).
Frolov, A. A., Mokienko, O. A., Lyukmanov, R., Chernikova, L. A., Kotov, S. V., Turbina, L. G., Bobrov, L. D., Biryukova, E. V., et al., “Preliminary results of a controlled study of the effectiveness of BCI-exoskeleton technology in poststroke paresis of the arm,” Vestn. Ros. Gos. Med. Univ., 2, 17–25 (2016).
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., 94, 97–109 (2006).
Frolov, A. A., Roschin, V. Y., and Biryukova, E. V., “Adaptive neural network model of multijoint movement control by working point velocity,” Neural Network World, 4, No. 2, 141–156 (1994).
Frolov, A., Húsek, D., Bobrov, P., Mokienko, O., and Tintera, J., “Sources of electrical brain activity most relevant to performance of brain–computer interface based on motor imagery,” in: Brain–Computer Interface Systems – Recent Progress and Future Prospects (2013), pp. 175–193.
Frolov, A., Misek, D., Bobrov, P., Korshakov, A., Chernikova, L., Konovalov, R., and Mokienko, O., “Sources of EEG activity most relevant to performance of brain–computer interface based on motor imagery,” Neural Network World, 22, No. 1, 21–37 (2012).
Fugl-Meyer, A. R., Jääskö, L., Leyman, L., Olsson, S., and Stegling, S., “The poststroke hemiplegic patient. A method for evaluation of physical performance,” Scand. J. Rehabil. Med., 7, 13–31 (1975).
Gladstone, D. J., Daniells, C. J., and Black, S. E., “The Fugl-Meyer Assessment of motor recovery alter stroke: A critical review of its measurement properties,” Neurorehabil. Neural Repair, 16, 232–240 (2002).
Gomi, H. and Kawato, M., “Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement,” Science, 272, 117–120 (1996).
Grèzes, J. and Decety, J., “Functional anatomy of execution, mental simulation, observation verb generation of actions: a meta-analysis,” Hum. Brain Mapp., 12, No. 1, 1–19 (2001).
Grosse-Wentrup, M., Mattia, D., and Oweiss, K., “Using brain–computer interfaces to induce neural plasticity and restore function,” J. Neural Eng., 8, No. 2, 025004 (2011).
Heo, P., Gu, G. M., Lee, S., Rhee, K., and Kim, J., “Current hand exoskeleton technologies for rehabilitation and assistive engineering,” Int. J. Precision Eng. Manufact., 13, No. 5, 807–824 (2012).
Hesse, S., Kuhlmann, H., Wilk, J., Tomelleri, C., and Kirker, S. G. B., “A new electromechanical trainer for sensorimotor rehabilitation of paralysed fi ngers: a case series in chronic and acute stroke patients,” J. Neuroeng. Rehabil., 5, 21 (2008).
Hesse, S., Schulte-Tigges, G., Konrad, M., Bardeleben, A., and Werner, C., “Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects,” Arch. Phys. Med. Rehabil., 84, No. 6, 915–920 (2003).
Hétu, S., Gregoire, M., Saimpont, A., Coll, M. P., Eugène, F., Michon, P. E., and Jackson, P. L., “The neural network of motor imagery: An ALE meta-analysis,” Neurosci. Biobehav. Rev., 37, 930–949 (2013).
Ho, N. S. K., Tong, K. Y. Hu, X. L., Fung, K. L., Wei, X. J., Rong, W., and Susanto, E. A., “An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation,” IEEE Int. Conf. Rehabil. Robot, Boston, MA (2011), 2011:5975340.
Hogan, N. and Flash, T., “Moving gracefully: quantitative theories of motor coordination,” Trends Neurosci., 10, 170–174 (1987).
Jack, D., Boian, R., Merlans, A. S., Tremaine, M., Burdea, G. C., Adamovich, S. V., Recce, M., and Poizner, H., “Virtual reality-enhanced stroke rehabilitation,” Neural Syst. Rehabil. Eng. IEEE Trans., 9, No. 3, 308–318 (2001).
Jeannerod, M. and Frak, V., “Mental imaging of motor activity in humans,” Curr. Opin. Neurobiol., 9, 735–739 (1999).
Jeannerod, M., “The representing brain: Neural correlates of motor intention and imagery,” Behav. and Brain Sci, 17, No. 2, 187–202 (1994).
Kachenoura, A., Albera, L., Senhadji, L., and Common, P., “ICA: a potential tool for BCI systems,” IEEE Signal Process. Mag., 25, No. 1, 57–68 (2008).
Kaneko, Y., Nakano, E., Osu, R., Wada, Y., and Kawato, M., “Trajectory formation based on the minimum commanded torque change model using the Euler-Poisson equation,” Systems and Computers in Japan, 36, 92–103 (2005).
Klimesch, W., “Alpha-band oscillations, attention-controlled access to stored information,” Trends Cogn. Sci., 16, No. 12, 606–617 (2012).
Klimesch, W., Sauseng, P., and Hanslmayr, S., “EEG alpha oscillations: the inhibition-timing hypothesis,” Brain Res. Rev., 53, No. 1, 63–88 (2007).
Koeneman, E. J., Schultz, R. S., Wolf, S. L., Herring, D. E., and Koeneman, J. B., “A pneumatic muscle hand therapy device,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 4, 2711–2713 (2004).
Kondur, A. A., Biryukova, E. V., Kotov, S. V., Turbina, L. G., and Frolov, A. A., “Kinematic portrait of the patient as an objective measure of the state of motor function during the process of neurorehabilitation using an arm exoskeleton controlled by a brain–computer interface,” Uchen. Zapiski, St. Petersb. Med. Univ. im. I. P. Pavlova, 23, No. 3, 28–31 (2016).
Kotov, S. V. and Stakhovskaya, L. V., Stroke, MIA, Moscow (2014).
Kotov, S. V., Turbina, L. G., Bobrov, P. D., Frolov, A. A., Pavlova, O. G., Kurganskaya, M. E., and Biryukova, E. V., “Rehabilitation of stroke patients using a bioengineered ‘brain–computer interface + exoskeleton’ system,” Zh. Nevrol. Psikhiatr. im. S. S. Korsakova, 12, 66–72 (2014).
Krebs, H. I., Hogan, N., Aisen, M. L., and Volpe, B. T., “Robot-aided neurorehabilitation,” IEEE Trans. Rehabil. Eng., 6, 75–87 (1988).
Kwakkel, G. and Meskers, C. G. M., “Effects of robotic therapy of the arm after stroke,” Lancet Neurol., 13, No. 2, 132–133 (2014).
Leont’ev, A. N. and Zaporozhets, A. V., The Recovery of Movements, Sovetskaya Nauka, Moscow (1945).
Levin, M. F., “Interjoint coordination during pointing movements is disrupted in spastic hemiparesis,” Brain, 119, 281–294 (1996).
Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., Ringer, R. J., Wagner, T. H., et al., “Robot-assisted therapy for long-term upper-limb impairment after stroke,” New Engl. J. Med., 362, 1772–1783 (2010).
Lotze, M., Braun, C., Birbaumer, N., Anders, S., and Cohen, L. G., “Motor learning elicited by voluntary drive,” Brain, 126, No. 4, 866–872 (2003).
Lum, P. S., Burgar, C. G., Van der Loos, M., Shor, P. C., Majmundar, M., and Yap, R., “MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study,” J. Rehabil. Res. Dev., 43, No. 5, 631–642 (2006).
Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., and Leonhardt, S., “A survey on robotic devices for upper limb rehabilitation,” J. NeuroEng. Rehabil., 11, 3 (2014).
Makeig, S., Bell, A. J., Jung, T. P., and Sejnowski, T. J., “Independent component analysis of electroencephalographic data,” Adv. Neural Info. Proc. Syst., 8, 145–151 (1996).
Marchal-Crespo, L. and Reinkensmeyer, D. J., “Review of control strategies for robotic movement training after neurologic injury,” J. Neuroeng. Rehabil., 6, 20 (2009).
Masia, L., Krebs, H. I., Cappa, P., and Hogan, N., “Design, characterization, and impedance limits of a hand robot,” Proc. IEEE 10th Int. Conf. Rehabil. Robotics, (ICORR), Noordwijk, Netherlands (2007), pp. 1085–1089.
Mayr, A., Kofler, M., and Saltuari, L., “ARMOR: an electromechanical robot for upper limb training following stroke. A prospective randomized controlled pilot study,” Handchir. Mikrochir. Plast. Chir., 40, 66–73 (2008).
Micera, S., Carrozza, M., Guglielmelli, E., Cappiello, G., Zaccone, F., Freschi, C., Colombo, R., Mazzone, A., et al., “A simple robotic system for neurorehabilitation,” Autonomous Robots, 19, No. 3, 271–284 (2005).
Mokienko, O. A., Bobrov, P. D., Chernikova, L. A., and Frolov, A. A., “A motor imagery-based brain–computer interface in the rehabilitation of patients with hemiparesis,” Byull. Sibirsk. Med., 12, No. 2, 30–35 (2013).
Mokienko, O. A., Lyukmanov, R. Kh., Chernikova, L. A., Suponeva, N. A., Paradov, M. A., and Frolov, A. A., “A brain–computer interface: first experience in clinical use in Russia,” Fiziol. Cheloveka, 42, No. 1, 31–39 (2016).
Mulas, M., Folgheraiter, M., and Gini, G., “An EMG-controlled exoskeleton for hand rehabilitation,” Proc. 9th Int. Conf. Rehabil. Robotics ICORR, Chicago, IL, (2005), pp. 371–374.
Nef, T., Guidaili, M., Klamroth-Marganska, V., and Riener, R., “ARMin –exoskeleton robot for stroke rehabilitation,” World Congress on Medical Physics and Biomedical Engineering, September 7–12, IFMBE Proceedings, Dössel, O. and Schlegel, W. C. (eds.), Springer, Munich, Germany (2009), pp. 127–130.
Nudo, R. J., Milliken, G. W., Jenkins, W. M., and Merzenich, M. M., “Usedependent alterations of movement representations in primary motor cortex of adult squirrel monkeys,” J. Neurosci., 16, No. 2, 785–807 (1996).
Ono, T., Shindo, K., Kawashima, K., Ota, N., Ito, M., Ota, T., Mukaino, M., Fujiwara, T., Kimura, A., Liu, M., et al., “Brain–computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke,” Front. Neuroeng., 7, 19 (2014).
Onton, J., Westerfi eld, M., Townsend, J., and Makeig, S., “Imaging human EEG dynamics using independent component analysis,” Neurosci. Biobehav. Rev., 30, No. 6, 808–822 (2006).
Patton, J. L., Small, S. L., and Rymer, W. Z., “Functional restoration for the stroke survivor: informing the efforts of engineers,” Top Stroke Rehabil., 15, No. 6, 521–541 (2008).
Patton, J. L., Stoykov, M. E., Kovic, M., and Mussa-Ivaldi, F., “Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors,” Exp. Brain Res., 168, No. 3, 368–383 (2006).
Pedrocchi, A., Ferrante, S., Ambrosini, E., Gandolla, M., Casellato, C., Schauer, T., Klauer, C., Pascual, J., et al., “MUNDUS project: Multimodal neuroprosthesis for daily upper limb support,” J. Neuroeng. Rehabil., 10, 66 (2013).
Peterka, R. J., “Sensorimotor integration in human postural control,” J. Neurophysiol., 88, 1097–1118 (2002).
Pfurtscheller, G. and Berghold, A., “Patterns of cortical activation during planning of voluntary movement,” Electroencephalogr. Clin. Neurophysiol., 72, 250–258 (1989).
Pfurtscheller, G. and Lopes da Silva, F. H., “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clin. Neurophysiol., 110, 1842–1857 (1999).
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.) Williams and Wilkins, Baltimore, MD (1999), 4th edition, pp. 958–967.
Pignolo, L., Dolce, G., Basta, G., Lucca, L. F., Serra, S., and Sannitam W. G., “Upper limb rehabilitation after stroke: ARAMIS a ‘robo-mechatronic’ innovative approach and prototype,” 4th IEEE RAS & EMBS Int. Conf. Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy (2012), pp. 1410–1414.
Ramos-Murguialday, A., Broetz, D., Rea, M., Laer, L., Yilmaz, O., Brasil, F. L., Liberati, G., Curado, M. R., et al., “Brain-machine interface in chronic stroke rehabilitation: a controlled study,” Ann. Neurol., 74, No. 1, 100–108 (2013).
Reinkensmeyer, D. J., Kahn, L. E., Averbuch, M., McKenna-Cole, A. N., Schmit, B. D., and Rymer, W. Z., “Understanding and treating arm movement impairment after chronic brain injury: Progress with the ARM Guides,” J. Rehabil. Res. Dev., 37, No. 6, 653–662 (2000).
Ren, Y., Park, H. S., and Zhang, L. Q., “Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation,” Proc. IEEE Int. Conf. Rehabil. Robotics (ICORR), Kyoto, Japan (2009), pp. 761–765. Restorative Neurology: Innovatory Technologies in Neurorehabilitation, Chernikova, L. A. (ed.), Medical Information Agency Press, Moscow (2016).
Riener, R., Nef, T., and Colombo, G., “Robot-aided neurorehabilitation of the upper extremities,” Med. Biol. Eng. Comput., 43, 2–10 (2005).
Rosati, G., Gallina, P., and Masiero, S., “Design, implementation and clinical tests of a wire-based robot for neurorehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng., 15, No. 4, 560–569 (2007).
Sanchez, R., Reinkensmeyer, D., Shah, P., Liu, J., Rao, S., Smith, R., Cramer, S., Rahman, T., and Bobrow, J., “Monitoring functional arm movement for home-based therapy after stroke,” Conf. Proc. IEEE Eng. Med. Biol. Soc. San Francisco, CA (2004), Vol. 7, pp. 4787–4790.
Schabowsky, C. N., Godfrey, S. B., Holley, R. J., and Lum, P. S., “Development and pilot testing of HEXORR: hand EXOskeleton rehabilitation robot,” J. Neuroeng. Rehabil., 7, 36 (2010).
Shaw, S. E., Morris, D. M., Uswatte, G., McKay, S., Meythaler, J. M., and Taub, E., “Constraint-induced movement therapy for recovery of upper-limb function following traumatic brain injury,” J. Rehabil. Res. Dev, 42, No. 6, 769–778 (2005).
Shelton, F. N. A. P. and Reding, M. J., “Effect of lesion location on upper limb motor recovery after stroke,” Stroke, 32, 107–112 (2001).
Shih, J. J., Krusienski, D. J., and Wolpaw, J. R., “Brain–computer interfaces in medicine,” Mayo Clin. Proc., 87, No. 3, 268–279 (2012).
Sinel’nikov, R. D., Atlas of Human Anatomy, Meditsina, Moscow (1967), Vol. 1.
Soekadar, S. R., Birbaumer, N., Slutzky, M. W., and Cohen, L. G., “Brainmachine interfaces in neurorehabilitation of stroke,” Neurobiol. Dis., 83, 172–179 (2015).
Stienen, A., Hekman, E., Prange, G., Jannink, M., Aalsma, A., van der Helm, F., van der Kooij, H., “Dampace: Design of an exoskeleton for force-coordination training in upper extremity rehabilitation,” J. Med. Devices, 3, 031003 (2009).
Stinear, C. M., “Prediction of recovery of motor function after stroke,” Lancet Neurol., 9, No. 12, 1228–1232 (2010).
Sun, H., Blakely, T. M., Darvas, F., Wander, J. D., Johnson, L. A., Su, D. K., Miller, K. J., Fetz, E. E., et al., “Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued fi nger movements,” Clin. Neurophysiol., 126, No. 11, 2150–2161 (2015).
Takahashi, C. D., Der-Yeghiaian, L., Le, V., Motiwala, R. R., and Cramer, S. C., “Robot-based hand motor therapy after stroke,” Brain, 131, 425–437 (2008).
Takemi, M., Masakado, Y., Liu, M., and Ushiba, J., “Event-related desynchronization reflects down-regulation of intracortical inhibition in human primary motor cortex,” J. Neurophysiol., 110, 1158–1166 (2013).
Taub, E., Uswatte, G., and Elbert, T., “New treatments in neurorehabilitation founded on basic research,” Nat. Rev. Neurosci., 3, No. 3, 228–236 (2002).
Teo, W. P. and Chew, E., “Is motor imagery brain–computer interface feasible in stroke rehabilitation?,” PM R, 6, 723–728 (2014).
Tong, K. Y., Ho, S. K., Pang, P. K., Hu, X. L., Tam, W. K., Fung, K. L., Wei, X. J., Chen, P. N., et al., “An intention driven hand functions task training robotic system,” Conf. Proc. IEEE Eng. Med. Biol. Soc. V. Buenos Aires, Argentina (2010), pp. 3406–3409.
Varkuti, B., Guan, C., Pan, Y., Phua, K. S., Ang, K. K., Kuah, C. W. K., Chua, K., Ang, B. T., et al., “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 (2013).
Wolbrecht, E. T., Leavitt, J., Reinkensmeyer, D. J., and Bobrow, J. E., “Control of a pneumatic orthosis for upper extremity stroke rehabilitation,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 1, 2687–2693 (2006).
Wright, Z. A., Rymer, W. Z., and Slutzky, M. W., “Reducing abnormal muscle coactivation after stroke using a myoelectric computer interface: A pilot study,” Neurorehabil. Neural Repair, 28, 443–451 (2014).
Zeiler, S. R. and Krakauer, J. W., “The interaction between training and plasticity in the post-stroke brain,” Curr. Opin. Neurol., 26, No. 6, 609–616 (2013).
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Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 67, No. 4, pp. 394–413, July–August, 2017
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Frolov, A.A., Kozlovskaya, I.B., Biryukova, E.V. et al. Use of Robotic Devices in Post-Stroke Rehabilitation. Neurosci Behav Physi 48, 1053–1066 (2018). https://doi.org/10.1007/s11055-018-0668-3
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DOI: https://doi.org/10.1007/s11055-018-0668-3