[Abstract + References] Brain Computer Interfaces in Rehabilitation Medicine – PM&R

Abstract

One innovation currently influencing physical medicine and rehabilitation is brain–computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user’s intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user’s interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.

References

  1. Wolpaw, J.R., Birbaumer, N., Heetderks, W.J. et al, Brain-computer interface technology: A review of the first international meeting. IEEE Trans Rehabil Eng2000;8:164–173.
  2. Kubanek, J., Miller, K., Ojemann, J., Wolpaw, J., Schalk, G. Decoding flexion of individual fingers using electrocorticographic signals in humans. J Neural Engin2009;6:066001.
  3. Arle JE, Shils JL, Malik WQ. Localized stimulation and recording in the spinal cord with microelectrode arrays. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE2012..

  4. Thakor, N.V. Translating the brain-machine interface. Sci Transl Med2013;5 (210ps217-210ps217).
  5. Irani, F., Platek, S.M., Bunce, S., Ruocco, A.C., Chute, D. Functional near infrared spectroscopy (fNIRS): An emerging neuroimaging technology with important applications for the study of brain disorders. Clin Neuropsychologist2007;21:9–37.
  6. Olson, J.D., Wander, J.D., Johnson, L. et al, Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans. Clin Neurophysiol2016;127:277–284.
  7. Olson, J.D., Wander, J.D., Darvas, F. Demonstration of motor-related beta and high gamma brain signals in subdermal electroencephalography recordings. Clin Neurophysiol2017;128:395–396.
  8. Schalk, G., Wolpaw, J.R., McFarland, D.J., Pfurtscheller, G. EEG-based communication: Presence of an error potential. Clin Neurophysiol2000;111:2138–2144.
  9. McFarland, D.J., McCane, L.M., Wolpaw, J.R. EEG-based communication and control: Short-term role of feedback. IEEE Trans Rehabil Eng1998;6:7–11.
  10. Widge, A.S., Moritz, C.T., Matsuoka, Y. Direct neural control of anatomically correct robotic hands. Brain-Computer Interfaces. Springer-VerlagBerlin, Heidelberg2010:105–119.
  11. Fetz, E.E. Volitional control of neural activity: Implications for brain–computer interfaces. J Physiol2007;579:571–579.
  12. Miller, K.J., Schalk, G., Fetz, E.E., Den Nijs, M., Ojemann, J.G., Rao, R.P. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci USA2010;107:4430–4435.
  13. Bouton, C.E., Shaikhouni, A., Annetta, N.V. et al, Restoring cortical control of functional movement in a human with quadriplegia. Nature2016;533:247–250.
  14. Sharma, G., Friedenberg, D.A., Annetta, N. et al, Using an artificial neural bypass to restore cortical control of rhythmic movements in a human with quadriplegia. Sci Rep2016;6:33807.
  15. Collinger, J.L., Boninger, M.L., Bruns, T.M., Curley, K., Wang, W., Weber, D.J. Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. J Rehabil Res Dev2013;50:145–160.
  16. Wodlinger, B., Downey, J.E., Tyler-Kabara, E.C., Schwartz, A.B., Boninger, M.L., Collinger, J.L. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: Difficulties, solutions, and limitations. J Neural Eng2015;12:016011.
  17. Friedenberg, D.A., Schwemmer, M.A., Landgraf, A.J. et al, Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human. Sci Rep2017;7:8386.
  18. Ajiboye, A.B., Willett, F.R., Young, D.R. et al, Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. Lancet2017;389:1821–1830.
  19. Batula, A.M., Mark, J.A., Kim, Y.E., Ayaz, H. Comparison of brain activation during motor imagery and motor movement using fNIRS. Comput Intell Neurosci2017;2017:5491296.
  20. Wu J, Casimo K, Caldwell DJ, Rao RP, Ojemann JG. Electrocorticographic dynamics predict visually guided motor imagery of grasp shaping. Paper presented at: Neural Engineering (NER), 2017 8th International IEEE/EMBS Conference on, 2017..

  21. Flesher, S., Downey, J., Collinger, J. et al, Intracortical microstimulation as a feedback source for brain-computer interface users. in: C. Guger, B. Allison, J. Ushiba (Eds.) Brain-Computer Interface ResearchSpringer International PublishingBasel2017:43–54.
  22. Luan, S., Williams, I., Nikolic, K., Constandinou, T.G. Neuromodulation: Present and emerging methods. Front Neuroeng2014;7:27.
  23. Nardone, R., Höller, Y., Taylor, A. et al, Noninvasive spinal cord stimulation: Technical aspects and therapeutic applications. Neuromodulation2015;18:580–591.
  24. Cronin, J.A., Wu, J., Collins, K.L. et al, Task-specific somatosensory feedback via cortical stimulation in humans. IEEE Trans Haptics2016;9:515–522.
  25. Collins, K.L., Guterstam, A., Cronin, J., Olson, J.D., Ehrsson, H.H., Ojemann, J.G. Ownership of an artificial limb induced by electrical brain stimulation. Proc Natl Acad Sci USA2017;114:166–171.
  26. O’Doherty, J.E., Lebedev, M.A., Ifft, P.J. et al, Active tactile exploration using a brain–machine–brain interface. Nature2011;479:228.
  27. Hiremath, S.V., Tyler-Kabara, E.C., Wheeler, J.J. et al, Human perception of electrical stimulation on the surface of somatosensory cortex. PLoS One2017;12:e0176020.
  28. Venkatakrishnan, A., Francisco, G.E., Contreras-Vidal, J.L. Applications of brain–machine interface systems in stroke recovery and rehabilitation. Curr Phys Med Rehabil Rep2014;2:93–105.
  29. Friedenberg, D.A., Bouton, C.E., Annetta, N.V. et al, Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface. Conf Proc IEEE Eng Med Biol Soc2016;:3084–3087.
  30. Friedenberg DA, Schwemmer M, Skomrock N, et al. Neural decoding algorithm requirements for a take-home brain computer interface. Conf Proc IEEE Eng Med Biol Soc, 2018, in press..

  31. Downey, J.E., Weiss, J.M., Muelling, K. et al, Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. J Neuroeng Rehabil2016;13:28.
  32. Knutson, J.S., Fu, M.J., Sheffler, L.R., Chae, J. Neuromuscular electrical stimulation for motor restoration in hemiplegia. Phys Med Rehabil Clin N Am2015;26:729.
  33. Ragnarsson, K. Functional electrical stimulation after spinal cord injury: Current use, therapeutic effects and future directions. Spinal Cord2008;46:255.
  34. Peckham, P.H., Keith, M.W., Kilgore, K.L. et al, Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: A multicenter study. Arch Phys Med Rehabil2001;82:1380–1388.
  35. Pool, D., Elliott, C., Bear, N. et al, Neuromuscular electrical stimulation-assisted gait increases muscle strength and volume in children with unilateral spastic cerebral palsy. Dev Med Child Neurol2016;58:492–501.
  36. Mulcahey, M.J., Betz, R.R., Kozin, S., Smith, B.T., Hutchinson, D., Lutz, C. Implantaton of the Freehand system during initial rehabilitation using minimally invasive techniques. Spinal Cord2004;42:146–155.
  37. Lauer, R.T., Peckham, P.H., Kilgore, K.L. EEG-based control of a hand grasp neuroprosthesis.Neuroreport1999;10:1767–1771.
  38. Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R. EEG-based neuroprosthesis control: A step towards clinical practice. Neurosci Lett2005;382:169–174.
  39. Pfurtscheller, G., Müller, G.R., Pfurtscheller, J., Gerner, H.J., Rupp, R. ‘Thought’—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett2003;351:33–36.
  40. Rohm, M., Schneiders, M., Müller, C. et al, Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artif Intell Med2013;59:133–142.
  41. Rupp, R., Rohm, M., Schneiders, M. et al, Think2grasp-bci-controlled neuroprosthesis for the upper extremity. Biomed Tech (Berl)2013; (https://doi.org/10.1515/bmt-2013-4440).
  42. Grimm, F., Walter, A., Spüler, M., Naros, G., Rosenstiel, W., Gharabaghi, A. Hybrid neuroprosthesis for the upper limb: Combining brain-controlled neuromuscular stimulation with a multi-joint arm exoskeleton. Front Neurosci2016;10:367.
  43. Burke, D., Gorman, E., Stokes, D., Lennon, O. An evaluation of neuromuscular electrical stimulation in critical care using the ICF framework: A systematic review and meta-analysis. Clin Respir J2016;10:407–420.
  44. Stein, C., Fritsch, C.G., Robinson, C., Sbruzzi, G., Plentz, R.D.M. Effects of electrical stimulation in spastic muscles after stroke: Systematic review and meta-analysis of randomized controlled trials.Stroke2015;46:2197–2205.
  45. Marquez-Chin, C., Marquis, A., Popovic, M.R. EEG-triggered functional electrical stimulation therapy for restoring upper limb function in chronic stroke with severe hemiplegia. Case Rep Neurol Med2016;2016:9146213.
  46. Rodrıguez, M., Pierre, C., Couve, S. et al, Towards brain–robot interfaces in stroke rehabilitation.PLoS One2011;6:1–17.
  47. Takahashi, M., Takeda, K., Otaka, Y. et al, Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study. J Neuroeng Rehabil2012;9:56.
  48. Knaut, L.A., Subramanian, S.K., McFadyen, B.J., Bourbonnais, D., Levin, M.F. Kinematics of pointing movements made in a virtual versus a physical 3-dimensional environment in healthy and stroke subjects. Arch Phys Med Rehabil2009;90:793–802.
  49. Laver, K.E., Lange, B., George, S., Deutsch, J.E., Saposnik, G., Crotty, M. Virtual reality for stroke rehabilitation. Stroke2018; (STROKEAHA.117.020275).
  50. Collinger, J.L., Wodlinger, B., Downey, J.E. et al, High-performance neuroprosthetic control by an individual with tetraplegia. Lancet2013;381:557–564.
  51. Tidoni, E., Abu-Alqumsan, M., Leonardis, D. et al, Local and remote cooperation with virtual and robotic agents: A P300 BCI study in healthy and people living with spinal cord injury. IEEE Trans Neural Syst Rehabil Eng2017;25:1622–1632.
  52. Colachis, S.C. IV, Bockbrader, M.A., Zhang, M. et al, Dexterous control of seven functional hand movements using cortically-controlled transcutaneous muscle stimulation in a person with tetraplegia. Front Neurosci2018;12:208.
  53. Saleh, S., Fluet, G., Qiu, Q., Merians, A., Adamovich, S.V., Tunik, E. Neural patterns of reorganization after intensive robot-assisted virtual reality therapy and repetitive task practice in patients with chronic stroke. Front Neurol2017;8:452.
  54. Fluet G, Patel J, Qinyin Q, et al. Early versus delayed VR-based hand training in persons with acute stroke. Paper presented at: 2017 International Conference on Virtual Rehabilitation (ICVR); June 19-22, 2017..

  55. Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., Neuper, C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci Lett2000;292:211–214.
  56. Lee, K., Liu, D., Perroud, L., Chavarriaga, R., Millán, JdR. A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers. Robotics Autonomous Systems2017;90:15–23.
  57. Sakurada, T., Kawase, T., Takano, K., Komatsu, T., Kansaku, K. A BMI-based occupational therapy assist suit: Asynchronous control by SSVEP. Front Neurosci2013;7:172.
  58. Chen, G., Chan, C.K., Guo, Z., Yu, H. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng2013;41:343–363.
  59. Louie, D.R., Eng, J.J. Powered robotic exoskeletons in post-stroke rehabilitation of gait: A scoping review. J Neuroeng Rehabil2016;13:53.
  60. Veerbeek, J.M., Langbroek-Amersfoort, A.C., van Wegen, E.E., Meskers, C.G., Kwakkel, G. Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis.Neurorehabil Neural Repair2017;31:107–121.
  61. McConnell, A.C., Moioli, R.C., Brasil, F.L. et al, Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke. J Rehabil Med2017;49:449–460.
  62. Pfurtscheller, G., Allison, B., Bauernfeind, G. et al, The hybrid BCI. Front Neurosci2010;4:30.
  63. Galán, F., Nuttin, M., Lew, E. et al, A brain-actuated wheelchair: Asynchronous and non-invasive brain–computer interfaces for continuous control of robots. Clin Neurophysiol2008;119:2159–2169.
  64. Raspopovic, S., Capogrosso, M., Petrini, F.M. et al, Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci Transl Med2014;6 (222ra219-222ra219).
  65. Flesher, S.N., Collinger, J.L., Foldes, S.T. et al, Intracortical microstimulation of human somatosensory cortex. Sci Transl Med2016;8 (361ra141-361ra141).
  66. Jezernik, S., Colombo, G., Keller, T., Frueh, H., Morari, M. Robotic orthosis lokomat: A rehabilitation and research tool. Neuromodulation2003;6:108–115.
  67. Daly, J.J., Wolpaw, J.R. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol2008;7:1032–1043.
  68. Dobkin, B.H. Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol2007;579:637–642.
  69. Bamdad, M., Zarshenas, H., Auais, M.A. Application of BCI systems in neurorehabilitation: A scoping review. Disabil Rehabil2015;10:355–364.
  70. Sellers, E.W., Ryan, D.B., Hauser, C.K. Noninvasive brain-computer interface enables communication after brainstem stroke. Sci Transl Med2014;6 (257re257-257re257).
  71. Wang, F., He, Y., Qu, J. et al, Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness. J Neural Engin2017;14:046024.
  72. Hochberg, L.R., Serruya, M.D., Friehs, G.M. et al, Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature2006;442:164–171.
  73. Tankus, A., Yeshurun, Y., Flash, T., Fried, I. Encoding of speed and direction of movement in the human supplementary motor area. J Neurosurg2009;110:1304–1316.
  74. Wang Y, Hong B, Gao X, Gao S. Phase synchrony measurement in motor cortex for classifying single-trial EEG during motor imagery. Paper presented at: Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE2006..

  75. Hermes, D., Vansteensel, M.J., Albers, A.M. et al, Functional MRI-based identification of brain areas involved in motor imagery for implantable brain–computer interfaces. J Neural Engin2011;8:025007.
  76. Wang, W., Collinger, J.L., Degenhart, A.D. et al, An electrocorticographic brain interface in an individual with tetraplegia. PLoS One2013;8:e55344.
  77. Wang Y, Makeig S. Predicting intended movement direction using EEG from human posterior parietal cortex. Paper presented at: International Conference on Foundations of Augmented Cognition, 2009..

  78. Klaes, C., Kellis, S., Aflalo, T. et al, Hand shape representations in the human posterior parietal cortex. J Neurosci2015;35:15466–15476.
  79. Broetz, D., Braun, C., Weber, C., Soekadar, S.R., Caria, A., Birbaumer, N. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: A case report.Neurorehabil Neural Repair2010;24:674–679.
  80. Ramos-Murguialday, A., Broetz, D., Rea, M. et al, Brain–machine interface in chronic stroke rehabilitation: A controlled study. Ann Neurol2013;74:100–108.
  81. Ang, K.K., Guan, C., Phua, K.S. et al, Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: Results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng2014;7:30.
  82. Heasman, J., Scott, T., Kirkup, L., Flynn, R., Vare, V., Gschwind, C. Control of a hand grasp neuroprosthesis using an electroencephalogram-triggered switch: Demonstration of improvements in performance using wavepacket analysis. Med Biol Eng Comput2002;40:588–593.
  83. Onose, G., Grozea, C., Anghelescu, A. et al, On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up. Spinal Cord2012;50:599.
  84. Kreilinger A, Kaiser V, Rohm M, Rupp R, Müller-Putz GR. BCI and FES training of a spinal cord injured end-user to control a neuroprosthesis. Biomed Tech (Berl), 2013. https://doi.org/10.1515/bmt-2013-4443..

  85. Downey, J.E., Brane, L., Gaunt, R.A., Tyler-Kabara, E.C., Boninger, M.L., Collinger, J.L. Motor cortical activity changes during neuroprosthetic-controlled object interaction. Sci Rep2017;7:16947.
  86. Kennedy, P.R., Bakay, R.A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport1998;9:1707–1711.
  87. Spataro, R., Chella, A., Allison, B. et al, Reaching and grasping a glass of water by locked-In ALS patients through a BCI-controlled humanoid robot. Front Hum Neurosci2017;11:68.
  88. Hochberg, L.R., Bacher, D., Jarosiewicz, B. et al, Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature2012;485:372–375.
  89. Keith, M.W., Peckham, P.H., Thrope, G.B., Buckett, J.R., Stroh, K.C., Menger, V. Functional neuromuscular stimulation neuroprostheses for the tetraplegic hand. Clin Orthop Relat Res1988;233:25–33.
  90. Bockbrader, M., Kortes, M.J., Annetta, N. et al, Implanted brain-computer interface controlling a neuroprosthetic for increasing upper limb function in a human with tetraparesis. PM R2016;8:S242–S243.
  91. Wang, P.T., King, C.E., Chui, L.A., Do, A.H., Nenadic, Z. Self-paced brain–computer interface control of ambulation in a virtual reality environment. J Neural Engin2012;9:056016.
  92. Louie, D.R., Eng, J.J., Lam, T. Gait speed using powered robotic exoskeletons after spinal cord injury: A systematic review and correlational study. J Neuroeng Rehabil2015;12:82.
  93. He Y, Nathan K, Venkatakrishnan A, et al. An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE2014..

  94. Osuagwu, B.C., Wallace, L., Fraser, M., Vuckovic, A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: A randomised pilot study. J Neural Engin2016;13:065002.
  95. Proietti, T., Crocher, V., Roby-Brami, A., Jarrassé, N. Upper-limb robotic exoskeletons for neurorehabilitation: a review on control strategies. IEEE Rev Biomed Engin2016;9:4–14.
  96. Donati, A.R., Shokur, S., Morya, E. et al, Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci Rep2016;6:30383.
  97. Buch, E., Weber, C., Cohen, L.G. et al, Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke2008;39:910–917.
  98. Caria, A., Weber, C., Brötz, D. et al, Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology2011;48:578–582.
  99. Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci2012;6:39.
  100. Várkuti, B., Guan, C., Pan, Y. et al, Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair2013;27:53–62.
  101. Zondervan, D.K., Augsburger, R., Bodenhoefer, B., Friedman, N., Reinkensmeyer, D.J., Cramer, S.C.Machine-based, self-guided home therapy for individuals with severe arm impairment after stroke: A randomized controlled trial. Neurorehabil Neural Repair2015;29:395–406.
  102. Chen, Y.-P., Howard, A.M. Effects of robotic therapy on upper-extremity function in children with cerebral palsy: A systematic review. Dev Neurorehabil2016;19:64–71.
  103. Dolbow, J.D., Mehler, C., Stevens, S.L., Hinojosa, J. Robotic-assisted gait training therapies for pediatric cerebral palsy: A review. J Rehabil Robotics2016;4:14–21.
  104. Hu, X.L., Tong, K.-y., Song, R., Zheng, X.J., Leung, W.W. A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke. Neurorehabil Neural Repair2009;23:837–846.
  105. Young, B.M., Nigogosyan, Z., Walton, L.M. et al, Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng2014;7:26.
  106. Sullivan JL, Bhagat NA, Yozbatiran N, et al. Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial. Paper presented at: Rehabilitation Robotics (ICORR), 2017 International Conference, 2017..

  107. Johansson, R.S., Flanagan, J.R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat Rev Neurosci2009;10:345.
  108. Monzée, J., Lamarre, Y., Smith, A.M. The effects of digital anesthesia on force control using a precision grip. J Neurophysiol2003;89:672–683.
  109. Johansson, R., Hager, C., Backstrom, L. Somatosensory control of precision grip during unpredictable pulling loads III. Impairments during digital anaesthesia. Exp Brain Res1992;89:204–213.
  110. Vaso, A., Adahan, H.-M., Gjika, A. et al, Peripheral nervous system origin of phantom limb pain.Pain2014;155:1384–1391.
  111. Polikandriotis, J.A., Hudak, E.M., Perry, M.W. Minimally invasive surgery through endoscopic laminotomy and foraminotomy for the treatment of lumbar spinal stenosis. J Orthop2013;10:13–16.
  112. Alimi, M., Njoku, I. Jr., Cong, G.-T. et al, Minimally invasive foraminotomy through tubular retractors via a contralateral approach in patients with unilateral radiculopathy. Op Neurosurg2014;10:436–447.
  113. Pirris, S.M., Dhall, S., Mummaneni, P.V., Kanter, A.S. Minimally invasive approach to extraforaminal disc herniations at the lumbosacral junction using an operating microscope: Case series and review of the literature. Neurosurg Focus2008;25:E10.
  114. Cruccu, G., Aziz, T., Garcia-Larrea, L. et al, EFNS guidelines on neurostimulation therapy for neuropathic pain. Eur J Neurol2007;14:952–970.
  115. Liem, L., Russo, M., Huygen, F.J. et al, A multicenter, prospective trial to assess the safety and performance of the spinal modulation dorsal root ganglion neurostimulator system in the treatment of chronic pain. Neuromodulation2013;16:471–482.
  116. Deer, T.R., Grigsby, E., Weiner, R.L., Wilcosky, B., Kramer, J.M. A prospective study of dorsal root ganglion stimulation for the relief of chronic pain. Neuromodulation2013;16:67–72.
  117. Eldabe, S., Burger, K., Moser, H. et al, Dorsal root ganglion (DRG) stimulation in the treatment of phantom limb pain (PLP). Neuromodulation2015;18:610–617.
  118. Lynch, P.J., McJunkin, T., Eross, E., Gooch, S., Maloney, J. Case report: Successful epiradicular peripheral nerve stimulation of the C2 dorsal root ganglion for postherpetic neuralgia.Neuromodulation2011;14:58–61.
  119. Tan, D., Tyler, D., Sweet, J., Miller, J. Intensity modulation: A novel approach to percept control in spinal cord stimulation. Neuromodulation2016;19:254–259.
  120. Tan, D.W., Schiefer, M.A., Keith, M.W., Anderson, J.R., Tyler, J., Tyler, D.J. A neural interface provides long-term stable natural touch perception. Sci Transl Med2014;6 (257ra138-257ra138).
  121. Viswanathan, A., Phan, P.C., Burton, A.W. Use of spinal cord stimulation in the treatment of phantom limb pain: Case series and review of the literature. Pain Practice2010;10:479–484.
  122. Berg, J., Dammann, J. III, Tenore, F. et al, Behavioral demonstration of a somatosensory neuroprosthesis. IEEE Trans Neural Syst Rehabil Eng2013;21:500–507.
  123. Kim, S., Callier, T., Tabot, G.A., Gaunt, R.A., Tenore, F.V., Bensmaia, S.J. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc Natl Acad Sci USA2015;112:15202–15207.
  124. Kim, S., Callier, T., Tabot, G.A., Tenore, F.V., Bensmaia, S.J. Sensitivity to microstimulation of somatosensory cortex distributed over multiple electrodes. Front Syst Neurosci2015;9:47.
  125. Callier, T., Schluter, E.W., Tabot, G.A., Miller, L.E., Tenore, F.V., Bensmaia, S.J. Long-term stability of sensitivity to intracortical microstimulation of somatosensory cortex. J Neural Engin2015;12:056010.
  126. Yuste, R., Goering, S., Agüera y Arcas, B. et al, Four ethical priorities for neurotechnologies and AI.Nature2017;551:159–163.
  127. Klein, E. Informed consent in implantable BCI research: Identifying risks and exploring meaning.Sci Engin Ethics2016;22:1299–1317.

 

via Brain Computer Interfaces in Rehabilitation Medicine – PM&R

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