Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders
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  • 作者:Frédéric D. Broccard (1) (2)
    Tim Mullen (3)
    Yu Mike Chi (4)
    David Peterson (1) (5)
    John R. Iversen (3)
    Mike Arnold (6)
    Kenneth Kreutz-Delgado (3)
    Tzyy-Ping Jung (3)
    Scott Makeig (3)
    Howard Poizner (1)
    Terrence Sejnowski (1) (5) (7)
    Gert Cauwenberghs (1) (2)
  • 关键词:Brain–machine–body interface ; Closed ; loop systems ; Movement disorders ; Noninvasive ; Rehabilitation
  • 刊名:Annals of Biomedical Engineering
  • 出版年:2014
  • 出版时间:August 2014
  • 年:2014
  • 卷:42
  • 期:8
  • 页码:1573-1593
  • 全文大小:3,456 KB
  • 参考文献:1. Afshar, P., A. Khambhati, S. Stanslaski, D. Carlson, R. Jensen, D. Linde, S. Dani, M. Lazarewicz, P. Cong, J. Giftakis, P. Stypulkowski, and T. Denison. A translational platform for prototyping closed-loop neuromodulation systems. / Front. Neural Circuits 6:117, 2013. CrossRef
    2. Albanese, A., K. Bhatia, S. B. Bressman, M. R. DeLong, S. Fahn, V. S. C. Fung, M. Hallett, J. Jankovic, H. A. Jinnah, C. Klein, A. E. Lang, J. W. Mink, and J. K. Teller. Phenomenology and classification of dystonia: a consensus update. / Mov. Disord. 28:863-73, 2013. CrossRef
    3. Alberts, J. L., C. Voelcker-Rehage, K. Hallahan, M. Vitek, R. Bamzai, and J. L. Vitek. Bilateral subthalamic stimulation impairs cognitive-motor performance in Parkinson’s disease patients. / Brain 131:3348-360, 2008. CrossRef
    4. Ashby, R. An Introduction to Cybernetics. London: Chapman & Hall, 1956.
    5. Astrom, K. J., and B. Wittenmark. Adaptive Control (2nd ed.). Hoboken, New Jersey: Addison-Wesley, 1994.
    6. Bai, O., M. Nakamura, and H. Shibasaki. Compensation of hand movement for patients by assistant force: relationship between human hand movement and robot arm motion. / IEEE Trans. Neural Sys. Rehabil. Eng. 9(3):302-07, 2001. CrossRef
    7. Baram, Y. Walking on tiles. / Neural Process. Lett. 10:81-7, 1999. CrossRef
    8. Baram, Y., J. Aharon-Peretz, Y. Simionovici, and L. Ron. Walking on virtual tiles. / Neural Process. Lett. 16:227-33, 2002. CrossRef
    9. Baram, Y., and A. Miller. Virtual reality cues for improvement of gait in patients with multiple sclerosis. / Neurology 66:178-81, 2006. CrossRef
    10. Bashashati, A., M. Fatourechi, R. K. Ward, and G. E. Birsh. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. / J. Neural Eng. 4:R32–R57, 2007. CrossRef
    11. Berns, G. S., and T. S. Sejnowski. A computational model of how the basal ganglia produce sequences. / J. Cogn. Neurosci. 10:108-21, 1998. CrossRef
    12. Blankertz, B., G. Curio, and K. R. Muller. Classifying single trial EEG: Towards brain–computer interfacing. In: Advances in Neural Information Processing Systems 14, edited by Dietterich, T. G., Becker S., and Ghahramani, Z. Cambridge, MA: MIT Press, 2002, pp. 157-64.
    13. Boahen, K. A. Point-to-point connectivity between neuromorphic chips using address-events. / IEEE Trans. Circuits Syst. II 47:416-34, 2000. CrossRef
    14. Bogacz, R., and T. Larsen. Integration of reinforcement learning and optimal decision-making theories of the basal ganglia. / Neural Comput. 23:817-51, 2011. CrossRef
    15. Bradberry, T. J., R. J. Gentili, and J. L. Contreras-Vidal. Fast attainment of computer cursor control with noninvasively acquired brain signals. / J. Neural Eng. 8:036010, 2011. CrossRef
    16. Brittain, J. S., P. Robert-Smith, T. Z. Aziz, and P. Brown. Tremor suppression by rhythmic transcranial current stimulation. / Curr. Biol. 23:436-40, 2013. CrossRef
    17. Carabalona, R., P. Castiglioni, and F. Gramatica. Brain-computer interfaces and neurorehabilitation. / Stud. Health Technol. Inf. 145:160-76, 2009.
    18. Carmena, J. M., M. A. Lebedev, R. E. Crist, J. E. O’Doherty, D. M. Santucci, D. F. Dimitrov, P. G. Patil, C. S. Henriquez, and M. A. Nicolelis. Learning to control a brain–machine interface for reaching and grasping in primates. / PLoS Biol. 1:E42, 2003. CrossRef
    19. Casadio, M., A. Pressman, S. Acosta, Z. Danziger, A. Fishbach, F. A. Mussa-Ivaldi, K. Muir, H. Tseng, and D. Chen. Body machine interface: remapping motor skills after spinal cord injury. In: Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR-1), Zurich, Switzerland, June/July, 2011.
    20. Casadio, M., R. Ranganathan, and F. Mussa-Ivaldi. The body–machine interface: a new perspective on an old theme. / J. Mot. Behav. 44:419-33, 2012. CrossRef
    21. Chakravarthy, V. S., D. Joseph, and R. S. Bapi. What do the basal ganglia do? A modeling perspective. / Biol. Cybern. 103:237-53, 2010. CrossRef
    22. Chapin, J. K., K. A. Moxon, R. S. Markowitz, and M. A. Nicolelis. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. / Nat. Neurosci. 2(7):664-70, 1999. CrossRef
    23. Chi, Y. M., and G. Cauwenberghs. Micropower integrated bioamplifier and auto-ranging ADC for wireless and implantable medical instrumentation. In: Proceedings of the IEEE European Solid State Circuits Conference (ESSCIRC-0), Sevilla, Spain, September 13-7, 2010.
    24. Chi, Y. M., and G. Cauwenberghs. Wireless non-contact biopotential electrode. In: Proceedings Body Sensor Networks (BSN), BioPolis, Singapore, 7- June 2010.
    25. Chi, Y. M., T. P. Jung, and G. Cauwenberghs. Dry-contact and noncontact biopotential electrodes: methodological review. / IEEE Rev. Biomed. Eng. 3:106-20, 2010. CrossRef
    26. Chi, Y. M., C. Maier, and G. Cauwenberghs. Ultra-high input impedance, low noise integrated amplifier for noncontact biopotential sensing. / IEEE. J. Emerg. Select. Topics Circuits Syst. 1:526-35, 2011. CrossRef
    27. Chi, Y. M., Y.-T. Wang, Y. Wang, C. Maier, T.-P. Jung, and G. Cauwenberghs. Dry and noncontact EEG sensors for mobile brain–computer interfaces. / IEEE Trans. Neural Syst. Rehabil. Eng. 20:228-35, 2012. CrossRef
    28. Columbo, R., F. Pisano, A. Mazzone, C. Delconte, S. Micera, M. C. Carrozza, P. Dario, and G. Minuco. Design strategies to improve patient motivation during robot-aided rehabilitation. / J. Neuroeng. Rehabil. 4:3, 2007. CrossRef
    29. Contreras-Vidal, J. L., and G. E. Stelmach. A neural model of basal ganglia-thalamocortical relations in normal and parkinsonian movement. / Biol. Cybern. 73:467-76, 1995. CrossRef
    30. Cymbalyuk, G. S., G. N. Patel, R. L. Calabrese, S. P. Deweerth, and A. H. Cohen. Modeling alternation to synchrony with inhibitory coupling: a neuromorphic VLSI approach. / Neural Comput. 12:2259-278, 2000. CrossRef
    31. Daly, J. J., and J. R. Wolpaw. Brain-computer interfaces in neurological rehabilitation. / Lancet Neurol. 7:1032-043, 2008. CrossRef
    32. Dangi, S., A. L. Orsborn, H. G. Moorman, and J. M. Carmena. Design and analysis of closed-loop adaptation algorithms for brain–machine interfaces. / Neural Comput. 25:1693-731, 2013. CrossRef
    33. Deco, G., V. K. Jirsa, P. A. Robinson, M. Breakspear, and K. Friston. The dynamic brain: from spiking neurons to neural masses and cortical fields. / PLoS Comput. Biol. 4(8):e1000092, 2008. CrossRef
    34. del R. Millán, J. Adaptive brain interfaces. / Commun. ACM 46:75-0, 2003. CrossRef
    35. Delbruck, T. Silicon retina with correlation-based, velocity-tuned pixels. / IEEE Trans. Neural Netw. 4:529-41, 1993. CrossRef
    36. Delorme, A., and S. Makeig. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. / J. Neurosci. Methods 134:9-1, 2004. CrossRef
    37. Delorme, A., T. Mullen, C. Kothe, Z. Akalin Acar, N. Bigdely-Shamlo, A. Vankov, and S. Makeig. EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. / Comput. Intell. Neurosci. 2011:130714, 2011. CrossRef
    38. DiGiovanna, J., C. Mahmoudi, J. Fortes, J. C. Principe, and J. C. Sanchez. Coadaptive brain–machine interface via reinforcement learning. / IEEE Trans. Biomed. Eng. 56:54-4, 2009. CrossRef
    39. Doud, A. J., J. P. Lucas, M. T. Pisansky, and B. He. Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain–computer interface. / PLoS ONE 6:e26322, 2011. CrossRef
    40. Eberle, W., J. Penders, and R. Firat Yazicioglu. Closing the loop for deep brain stimulation implants enables personalized healthcare for Parkinsons disease patients. In: Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBS-1), Boston, Massachusetts USA, August 30–September 3, 2011.
    41. Elahi, B., B. Elahi, and R. Chen. Effect of transcranial magnetic stimulation on Parkinson motor function–systematic review of controlled clinical trials. / Mov. Disord. 24:357-63, 2009. CrossRef
    42. Emken, J. L., R. Benitez, and D. J. Reinkensmeyer. Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed. / J. Neuroeng. Rehabil. 4:8, 2007. CrossRef
    43. Emken, J. L., S. J. Harkema, J. Beres-Jones, C. K. Ferreira, and D. J. Reinkensmeyer. Feasibility of manual teach-and-replay and continuous impedance shaping for robotic locomotor training following spinal cord injury. / IEEE Trans. Biomed. Eng. 55:322-34, 2008. CrossRef
    44. Espay, A. J., Y. Baram, A. Kumar Dwivedi, R. Shukla, M. Gartner, L. Gaines, A. P. Duker, and F. J. Revilla. At-home training with closed-loop augmented-reality cueing device for improving gait in patients with Parkinson disease. / J. Rehabil. Res. Dev. 47:573-82, 2010. CrossRef
    45. Espay, A. J., L. Gaines, and R. Gupta. Sensory feedback in Parkinson’s disease with on-predominant freezing of gait. / Front. Neurol. 4:14, 2013. CrossRef
    46. Felton, E., R. Radwin, J. Wilson, and J. Williams. Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals. / J. Neural Eng. 6:056002, 2009. CrossRef
    47. Feng, X.-J., B. Greenwald, H. Rabitz, E. Shea-Brown, and R. Kosut. Towards closed-loop optimization of deep brain stimulation for Parkinson’s disease: concepts and lessons from a computational model. / J. Neural Eng. 4:L14–L21, 2007. CrossRef
    48. Feng, X.-J., E. Shea-Brown, B. Greenwald, R. Kosut, and H. Rabitz. Optimal deep brain stimulation of the subthalamic nucleus—a computational study. / J. Comp. Neurosci. 23:265-82, 2007. CrossRef
    49. Fregni, F., and A. Pascual-Leone. Technology insight: noninvasive brain stimulation in neurology—perspectives on the therapeutic potential of rTMS and tDCS. / Nat. Clin. Pract. Neurol. 3:383-93, 2007. CrossRef
    50. Fregni, R., D. K. Simon, A. Wu, and A. Pascual-Leone. Non-invasive brain stimulation for Parkinson’s disease: a systematic review and meta-analysis of the literature. / J. Neurol. Neurosurg. Psychiatry 6:1614-623, 2005. CrossRef
    51. Frucht, S. J. The definition of dystonia: current concepts and controversies. / Mov. Disord. 28:884-88, 2013. CrossRef
    52. Ganguly, K., and J. M. Carmena. Emergence of a stable cortical map for neuroprosthetic control. / PLoS Biol. 7:e1000153, 2009. CrossRef
    53. Ganguly, K., D. F. Dimitrov, J. D. Wallis, and J. M. Carmena. Reversible large-scale modification of cortical networks during neuroprosthetic control. / Nat. Neurosci. 14:662-67, 2011. CrossRef
    54. Gatev, P., and T. Wichmann. Interactions between cortical rhythms and spiking activity of single basal ganglia neurons in the normal and Parkinsonian state. / Cereb. Cortex 19(6):1330-344, 2009. CrossRef
    55. Gilja, V., P. Nuyujukian, C. A. Chestek, J. P. Cunningham, B. M. Yu, J. M. Fan, M. M. Churchland, M. T. Kaufman, J. C. Cao, S. I. Ryu, and K. V. Shenoy. A high-performance neural prosthesis enabled by control algorithm design. / Nat. Neurosci. 15:1752-757, 2012. CrossRef
    56. Gilja, V., P. Nuyujukian, C. Chestek, J. Cunningham, B. Yu, S. Ryu, and K. Shenoy. High-performance continuous neural cursor control enabled by feedback control perspective. In: Front. Neurosci. Comp. Syst. Neurosci. Conf., 2010.
    57. Goldberg, D. H., G. Cauwenberghs, and A. G. Andreou. Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons. / Neural Netw. 14:781-93, 2001. CrossRef
    58. Govil, N., A. Akinin, S. Ward, J. Snider, M. Plank, G. Cauwenberghs, and H. Poizner. The role of proprioceptive feedback in parkinsonian resting tremor. In: Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC-3), Osaka, Japan, 3- July 2013.
    59. Gramann, K., J. T. Gwin, N. Bigdely-Shamlo, D. P. Ferris, and S. Makeig. Visual evoked responses during standing and walking. / Front. Hum. Neurosci. 4:202, 2010. CrossRef
    60. Gramann, K., J. T. Gwin, D. P. Ferris, K. Oie, T.-P. Jung, C. T. Lin, L. D. Liao, and S. Makeig. Cognition in action: imaging brain/body dynamics in mobile humans. / Rev. Neurosci. 22(6):593-08, 2011.
    61. Guadagnoli, M. A., and T. D. Lee. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. / J. Motor Behav. 36(2):212-24, 2004. CrossRef
    62. Hale, K., and K. Stanney. Deriving haptic design guidelines from human physiological and neurological foundation. / IEEE Comput. Graph. Appl. 24(2):39, 2004. CrossRef
    63. Hasler, J., and B. Marr. Finding a roadmap to achieve large neuromorphic hardware systems. / Front. Neurosci. 7:118, 2013. CrossRef
    64. He, B., Y. Dai, L. Astolfi, F. Babiloni, H. Yuan, and L. Yang. eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. / J. Neurosci. Methods 195:261-69, 2011. CrossRef
    65. He, L., and C. Yang. Wilke, and H. Yuan. Electrophysiological imaging of brain activity and connectivity—challenges and opportunities. / IEEE Trans. Biomed. Eng. 58:1918-931, 2011. CrossRef
    66. Hikosaka, O., and M. Isoda. Switching from automatic to controlled behavior: cortico-basal ganglia mechanisms. / Trends Cogn. Sci. 14:154-61, 2010. CrossRef
    67. Hochberg, L. R., M. D. Serruya, G. M. Friehs, J. A. Mukand, M. Saleh, A. H. Caplan, A. Branner, D. Chen, R. D. Penn, and J. P. Donoghue. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. / Nature 442:164-71, 2005. CrossRef
    68. Holden, M. K. Virtual environment for motor rehabilitation: review. / CyberPsychol. Behav. 8(3):187-11, 2005. CrossRef
    69. IEEE EMB/CAS/SMC. Workshop on Brain–Machine–Body Interfaces, San Diego, CA, 27 August 2012, http://embc2012.embs.org/program/bmbi/.
    70. Jackson, A., J. Mavoori, and E. E. Fetz. Long-term motor cortex plasticity induced by an electronic neural implant. / Nature 444:56-0, 2006. CrossRef
    71. Jarosiewicz, B., S. M. Chase, G. W. Fraser, M. Velliste, R. E. Kass, and A. B. Schwartz. Functional network reorganization during learning in a brain–computer interface paradigm. / Proc. Natl. Acad. Sci. U.S.A. 105:19486-9491, 2008. CrossRef
    72. Jarosiewicz, B., N. Y. Masse, D. Bacher, S. S. Cash, E. Eskandar, G. Friehs, J. P. Donoghue, and L. R. Hochberg. Advantages of closed-loop calibration in intracortical brain–computer interfaces for people with tetraplegia. / J. Neural Eng. 10:046012, 2013. CrossRef
    73. Kahn, L. E., M. L. Zygman, W. Z. Rymer, and D. J. Reinkensmeyer. Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study. / J. Neuroeng. Rehabil. 3:12, 2006. CrossRef
    74. Koralek, A. C., R. M. Costa, and J. M. Carmena. Temporally precise cell-specific coherence develops in corticostriatal networks during learning. / Neuron 79(5):865-72, 2013. CrossRef
    75. Koralek, A. C., X. Jin, J. D. Long, II, R. M. Costa, and J. M. Carmena. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. / Nature 483:331-35, 2012. CrossRef
    76. Kothe, C. A., and S. Makeig. BCILAB: a platform for brain–computer interface development. / J. Neural Eng. 10:056014, 2013. CrossRef
    77. Krebs, H. I., and N. Hogan. Robotic therapy: the tipping point. / Am. J. Phys. Med. Rehabil. 91:S290–S297, 2012. CrossRef
    78. Krebs, H. I., J. J. Palazzolo, L. Dipietro, M. Ferraro, J. Krol, K. Rannekleiv, B. T. Volpe, and N. Hogan. Rehabilitation robotics: performance-based progressive robot-assisted therapy. / Autonom. Robots 15:7-0, 2003. CrossRef
    79. Kwakkel, G., B. J. Kollen, and H. I. Krebs. Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. / Neurorehabil. Neural Repair 22(2):111-21, 2008. CrossRef
    80. Lebedev, M. A., and M. A. Nicolelis. Brain-machine interfaces: past, present and future. / Trends Neurosci. 29(9):536-46, 2006. CrossRef
    81. Lewis, M. A., R. Etienne-Cummings, M. H. Hartmann, A. H. Cohen, and Z. R. Xu. An in silico central pattern generator: silicon oscillator, coupling, entrainment, physical computation and biped mechanism control. / Biol. Cybern. 88:137-51, 2003. CrossRef
    82. Li, F., P. Harmer, K. Fitzgerald, E. Eckstrom, R. Stock, J. Galver, G. Maddalozzo, and S. S. Batya. Tai Chi and postural stability in patients with Parkinson’s disease. / N. Engl. J. Med. 366:511-19, 2012. CrossRef
    83. Li, Z., J. E. O’Doherty, M. A. Lebedev, and M. A. Nicolelis. Adaptive decoding for brain–machine interfaces through Bayesian parameter updates. / Neural Comput. 23:3162-204, 2011. CrossRef
    84. Li, J., Y. Wang, L. Zhang, and T.-P. Jung. Combining ERPs and EEG spectral features for decoding intended movement direction. / Conf. Proc. IEEE Eng. Med. Biol. Soc. 2012:1769-772, 2012.
    85. Lima, L. O., A. Scianni, and F. Rodrigues-de-Paula. Progressive resistance exercise improves strength and physical performance in people with mild to moderate Parkinson’s disease: a systematic review. / J. Physiother. 59(1):7-3, 2013. CrossRef
    86. Little, S., and P. Brown. What brain signals are suitable for feedback control of deep brain stimulation in Parkinson’s disease? / Ann. N. Y. Acad. Sci. 1265:9-4, 2012. CrossRef
    87. Little, S., and P. Brown. The functional role of beta oscillations in Parkinson’s disease. / Parkinsonism Relat. Disord. 20(suppl 1):S44–S48, 2014. CrossRef
    88. Little, S., A. Pogosyan, S. Neal, B. Zavala, L. Zrinzo, M. Hariz, T. Foltynie, P. Limousin, K. Ashkan, J. FitzGerald, A. L. Green, T. Aziz, and P. Brown. Adaptive deep brain stimulation in advanced Parkinson disease. / Ann. Neurol. 74:449-57, 2013.
    89. Liu, C., and B. He. Noninvasive estimation of global activation sequence using the extended Kalman filter. / IEEE Trans. Biomed. Eng. 58:541-49, 2011. CrossRef
    90. Lo, A. C., V. C. Chang, M. A. Gianfrancesco, J. H. Friedman, T. S. Patterson, and D. F. Benedicto. Reduction of freezing of gait in Parkinson’s disease by repetitive robot-assisted treadmill training: a pilot study. / J. Neuroeng. Rehabil. 7:51, 2010. CrossRef
    91. Long, J., Y. Li, H. Wang, T. Yu, J. Pan, and F. Li. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. / IEEE Trans. Neural Syst. Rehabil. Eng. 20:720-29, 2012. CrossRef
    92. Lotte, F., M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi. A review of classification algorithms for EEG-based brain–computer interfaces. / J. Neural Eng. 4:R1–R13, 2007. CrossRef
    93. Lotze, M., C. Braun, N. Birbaumer, S. Anders, and L. G. Cohen. Motor learning elicited by voluntary drive. / Brain 126(4):866-72, 2003. CrossRef
    94. Lukos, J. R., J. Snider, M. E. Hernandez, E. Tunik, S. Hillyard, and H. Poizner. Parkinson’s disease patients show impaired corrective grasp control and eye-hand coupling when reaching to grasp virtual objects. / Neuroscience 254:205-21, 2013. CrossRef
    95. Lyons, K. E., and R. Pahwa. Pharmacotherapy of essential tremor: an overview of existing and upcoming agents. / CNS Drug 22:1037-045, 2008. CrossRef
    96. Lyons, K. E., R. Pahwa, C. L. Comella, M. S. Eisa, R. J. Elble, S. Fahn, J. Jankovic, J. L. Juncos, W. C. Koller, W. G. Ondo, K. D. Sethi, M. B. Stern, C. M. Tanner, R. Tintner, and R. L. Watts. Benefits and risks of pharmacological treatments for essential tremor. / Drug Saf. 26:461-81, 2003. CrossRef
    97. Mahmoudi, B., and J. C. Sanchez. A symbiotic brain–machine interface through value-based decision making. / PLoS ONE 6:e14760, 2011. CrossRef
    98. Makeig, S., K. Gramann, T.-P. Jung, T. J. Sejnowski, and H. Poizner. Linking brain, mind and behavior. / Int. J. Psychophysiol. 73:95-00, 2009. CrossRef
    99. Marchal-Crespo, L., and D. J. Reinkensmeyer. Review of control strategies for robotic movement training after neurologic injury. / J. Neuroeng. Rehabil. 6:20, 2009. CrossRef
    100. Mavoori, J., A. Jackson, C. Diorio, and E. Fetz. An autonomous implantable computer for neural recording and stimulation in unrestrained primates. / J. Neurosci. Methods 148:71-7, 2005. CrossRef
    101. Mead, C. Analog VLSI and Neural Systems. Hoboken, New Jersey: Addison-Wesley, 1989. CrossRef
    102. Minogue, J., and M. G. Jones. Haptics in education: exploring an untapped sensory modality. / Rev. Educ. Res. 76(3):3-7, 2006. CrossRef
    103. Modolo, J., A. Beuter, A. W. Thomas, and A. Legros. Using “smart stimulators-to treat Parkinson’s disease: re-engineering neurostimulation devices. / Front. Comput. Neurosci. 6:69, 2012. CrossRef
    104. Molier, B., E. Van Asseldonk, H. Hermens, and M. Jannink. Nature, timing, frequency and type of augmented feedback; does it influence motor relearning of the hemiparetic arm after stroke? A systematic review. / Disab. Rehabil. 32(22):1799-809, 2010. CrossRef
    105. Mullen, T., C. Kothe, Y. M. Chi, A. Ojeda, T. Kerth, S. Makeig, and T.-P. Jung. Real-time estimation and 3D visualization of source dynamics and connectivity using wearable EEG. In: Proceedings of the 35th Annual International Conference of the IEEE Engineering in Biology & Medicine Society (EBMS-3), Osaka, Japan, July 3-, 2013.
    106. Muller, K. R., G. Curio, B. Blankertz, and G. Dornhege. Combining features for BCI. In: Advances in Neural Information Processing Systems (NIPS02) 15, edited by Becker, S., Thrun, S., and Obermayer, K., British Columbia, Canada, 2003, pp. 1115-122.
    107. Mussa-Ivaldi, F. A., M. Casadio, and R. Ranganathan. The body–machine interface: a pathway for rehabilitation and assistance in people with movement disorders. / Expert Rev. Med. Devices 10:145-47, 2013. CrossRef
    108. O’Suilleabhain, P., J. Bullard, and R. B. Dewey. Proprioception in Parkinson’s disease is acutely depressed by dopaminergic medications. / J. Neurol. Neurosurg. Psychiatry 71:607-10, 2001. CrossRef
    109. Orsborn, A. L., and J. M. Carmena. Creating new functional circuits for action via brain–machine interfaces. / Front. Comp. Neurosci. 7:157, 2013.
    110. Orsborn, A. L., S. Dangi, H. G. Moorman, and J. M. Carmena. Exploring time-scales of closed-loop decoder adaptation in brain–machine interfaces. / Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011:5436-439, 2011.
    111. Orsborn, A. L., S. Dangi, H. G. Moorman, and J. M. Carmena. Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions. / IEEE Trans. Neural Syst. Rehabil. Eng. 20:468-77, 2012. CrossRef
    112. Oviatt, S., R. Coulston, and R. Lunsford. When do we interact multimodally? Cognitive load and multimodal communication patterns. In: Proceedings of the 6th International Conference on Multimodal Interfaces. New York: ACM, 2004, pp. 129-36.
    113. Pascual-Leone, A., A. Amedi, F. Fregni, and L. B. Merabet. The plastic human brain cortex. / Annu. Rev. Neurosci. 28:377-01, 2005. CrossRef
    114. Peterson, D. A., P. Berque, H. C. Jabusch, E. Altenmuller, and S. J. Frucht. Rating scales for musician’s dystonia: the state of the art. / Neurology 81:589-98, 2013. CrossRef
    115. Peterson, D. A., T. J. Sejnowski, and H. Poizner. Convergent evidence for abnormal striatal synaptic plasticity in dystonia. / Neurobiol. Dis. 37:558-73, 2010. CrossRef
    116. Petzinger, G. M., B. E. Fisher, S. McEwen, J. A. Beeler, J. P. Walsh, and M. W. Jakowec. Exercise-enhanced neuroplasticity targeting motor and cognitive circuitry in Parkinson’s disease. / Lancet Neurol. 12:716-26, 2013. CrossRef
    117. Pizzolato, G., and T. Mandat. Deep brain stimulation for movement disorders. / Front. Integr. Neurosci. 6:2, 2012. CrossRef
    118. Pollok, B., V. Krause, W. Martsch, C. Wach, A. Schnitzler, and M. Südmeyer. Motor-cortical oscillations in early stages of Parkinson’s disease. / J. Physiol. 590:3203-212, 2012. CrossRef
    119. Poor, H. V. An Introduction to Signal Detection and Estimation. New York: Springer, 1994. CrossRef
    120. Priori, A., G. Foffani, L. Rossi, and S. Marceglia. Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. / Exp. Neurol. 245:77-6, 2013. CrossRef
    121. Raja, M., and A. R. Bentivoglio. Impulsive and compulsive behaviors during dopamine replacement treatment in Parkinson’s disease and other disorders. / Curr. Drug Saf. 7:63-5, 2012. CrossRef
    122. Rasch, M., N. K. Logothetis, and G. Kreiman. From neurons to circuits: linear estimation of local field potentials. / J. Neurosci. 29(44):13785-3796, 2009. CrossRef
    123. Rascol, O., P. Payoux, F. Ory, J. J. Ferreira, C. Brefel-Courbon, and J. L. Montastruc. Limitations of current Parkinson’s disease therapy. / Ann. Neurol. 53(Supp. 3):S3–S12, 2003. CrossRef
    124. Redgrave, P., M. Rodriguez, Y. Smith, M. C. Rodriguez-Oroz, S. Lehericy, H. Bergman, Y. Agid, M. R. DeLong, and J. A. Obeso. Goal-directed and habitual control in the basal ganglia: implications for Parkinson’s disease. / Nat. Rev. Neurosci. 11:760-72, 2010. CrossRef
    125. Reinkensmeyer, D. J. How to retrain movement after neurologic injury: a computational rationale for incorporating robot (or therapist) assistance. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS, pp. 1479-482, 2003.
    126. Reinkensmeyer, D. J., J. L. Emken, and S. C. Cramer. Robotics, motor learning, and neurologic recovery. / Annu. Rev. Biomed. Eng. 6:497-25, 2004. CrossRef
    127. Rosin, B., M. Slovik, R. Mitelman, M. Rivlin-Etzion, S. N. Haber, Z. Israel, E. Vaadia, and H. Bergman. Closed-loop deep brain stimulation is superior in amerliorating Parkinsonism. / Neuron 72:370-84, 2011. CrossRef
    128. Rossini, P. M., and G. Dal Forno. Integrated technology for evaluation of brain function and neural plasticity. / Phys. Med. Rehabil. Clin. N. Am. 15(1):263-06, 2004. CrossRef
    129. Royer, A. S., and B. He. Goal selection versus process control in a brain–computer interface based on sensorimotor rhythms. / J. Neural Eng. 6:016005, 2009. CrossRef
    130. Salmoni, S. Knowledge of results and motor learning. A review and critical reappraisal. / Psychol. Bull. 95(3):355-86, 1984. CrossRef
    131. Sanchez, J. C., B. Mahmoudi, J. DiGiovanna, and J. C. Principe. Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants. / Neural Netw. 22:305-15, 2009. CrossRef
    132. Santaniello, S., G. Fiengo, L. Glielmo, and W. M. Grill. Closed-loop control of deep brain stimulation: a simulation study. / IEEE Trans. Neural Syst. Rehabil. Eng. 19:15-4, 2011. CrossRef
    133. Schiff, S. J. Towards model-based control of Parkinson’s disease. / Philos. Trans. A. Math. Phys. Eng. 368:2269-308, 2010. CrossRef
    134. Schmidt, R. A. Frequent augmented feedback can degrade learning: evidence and interpretations. / Tutorials Motor Neurosci. 62:59-5, 1991. CrossRef
    135. Serrano-Gotarredona, R., M. Oster, P. Lichtsteiner, A. Linares-Barranco, R. Paz-Vicente, F. Gomez-Rodriguez, L. Camunas-Mesa, R. Berner, M. Rivas, T. Delbruck, S.-C. Liu, R. Douglas, P. Haefliger, G. Jimenez-Moreno, A. Civit, T. Serrano-Gotarredona, A. Acosta-Jimenez, and B. Linares-Barranco. CAVIAR: a 45?k-neuron, 5?M-synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high speed visual object recognition and tracking. / IEEE Trans. Neural Netw. 20:1417-438, 2009. CrossRef
    136. Shpigelman, L., H. Lalazar, and E. Vaadia. Kernel-ARMA for hand tracking and brain-machine interfacing during 3D motor control. In: Proc. Neural Inf. Process. Syst., pp. 1489-496, 2008.
    137. Sigrist, R., G. Rauter, R. Riener, and P. Wolf. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. / Psychon. Bull. Rev. 20:21-3, 2013. CrossRef
    138. Snider, J., and M. Plank. Lee D, and H. Poizner. Simultaneous neural and movement recordings in large-scale immersive virtual environments. / IEEE Trans. Biomed. Circuits Syst. 7:713-21, 2013. CrossRef
    139. Snider, J., M. Plank, G. Lynch, E. Halgren, and H. Poizner. Human cortical θ during free exploration encodes space and predicts subsequent memory. / J. Neurosci. 33:15056-5068, 2013. CrossRef
    140. Snijders, A. H., I. Toni, E. Ruzicka, and B. R. Bloem. Bicycling breaks the ice for freezers of gait. / Mov. Disord. 26(3):367-71, 2011. CrossRef
    141. Stanslaski, S., P. Afshar, P. Cong, J. Giftakis, P. Stypulkowski, D. Carlson, D. Linde, D. Ullestad, A. T. Avestruz, and T. Denison. Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. / IEEE Trans. Neural Syst. Rehabil. Eng. 20:410-21, 2012. CrossRef
    142. Stein, J. K., J. Narendran, and K. McBean. Krebs, and R. Hughes. Electromyography-controlled exoskeletal upper-limb-powered orthosis for exercise and training after stroke. / Am. J. Phys. Med. Rehabil. 86(4):255-61, 2007. CrossRef
    143. Suminski, A. J., D. C. Tkach, A. H. Fagg, and N. G. Hatsopoulos. Incoporating feedback from multiple sensory modalities enhances brain–machine interface control. / J. Neurosci. 30(50):16777-6787, 2010. CrossRef
    144. Suminski, A. J., D. C. Tkach, and N. G. Hatsopoulos. Exploiting multiple sensory modalities in brain–machine interfaces. / Neural Netw. 22:1224-234, 2009. CrossRef
    145. Sutton, R. S., and A. G. Barto. Reinforcement Learning: An introduction. Cambridge, MA: MIT Press, 1998.
    146. Swann, N., H. Poizner, M. Houser, S. Gould, I. Greenhouse, W. Cai, J. Strunk, J. George, and A. R. Aron. Deep brain stimulation of the subthalamic nucleus alters the cortical profile of response inhibition in the beta frequency band: a scalp EEG study in Parkinson’s disease. / J. Neurosci. 31:5721-729, 2011. CrossRef
    147. Taylor, D. M., S. I. Tillery, and A. B. Schwartz. Direct cortical control of 3D neuroprosthetic devices. / Science 296:1829-832, 2002. CrossRef
    148. Tefertiller, C., B. Pharo, N. Evans, and P. Winchester. Efficacy of rehabilitation robotics for walking training in neurological disorders: a review. / J. Rehabil. Res. Dev. 48(4):387-16, 2011. CrossRef
    149. Thenganatt, M. A., and S. Fahn. Botulinum toxin for the treatment of movement disorders. / Curr. Neurol. Neurosci. 12:399-09, 2012. CrossRef
    150. Torres, E. B., K. M. Heilman, and H. Poizner. Impaired endogenously evoked automated reaching in Parkinson’s disease. / J. Neurosci. 31:17848-7863, 2011. CrossRef
    151. Townsend, G., B. LaPallo, C. Boulay, D. Krusienski, G. Frye, C. Hauser, N. E. Schwartz, T. M. Vaughan, J. R. Wolpaw, and E. W. Sellers. A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. / Clin. Neurophysiol. 121:1109-120, 2010. CrossRef
    152. Tubiana, R. Musician’s focal dystonia. / Hand Clin. 19:303-08, 2003. CrossRef
    153. Tunik, E., A. G. Feldman, and H. Poizner. Dopamine replacement therapy does not restore the ability of Parkinsonian patients to make rapid adjustments in motor strategies according to changing sensorimotor contexts. / Parkinsonism Relat. Disord. 13:425-33, 2007. CrossRef
    154. Ustinova, K., L. Chernikova, A. Bilimenko, A. Telenkov, and N. Epstein. Effect of robotic locomotor training in an individual with Parkinson’s disease: a case report. / Disabil. Rehabil. Assist. Technol. 6(1):77-5, 2011. CrossRef
    155. Vogelstein, R. J., U. Mallik, E. Culurciello, G. Cauwenberghs, and R. Etienne-Cummings. A multi-chip neuromorphic system for spike-based visual information processing. / Neural Comput. 19:2281-300, 2007. CrossRef
    156. Vogelstein, R. J., U. Mallik, J. T. Vogelstein, and G. Cauwenberghs. Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses. / IEEE Trans. Neural Netw. 18:253-65, 2007. CrossRef
    157. Wang, W., J. L. Collinger, M. A. Perez, E. C. Tyler-Kabara, L. G. Cohen, N. Birbaumer, S. W. Brosse, A. B. Schwartz, M. L. Boninger, and D. J. Weber. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. / Phys. Med. Rehabil. Clin. N. Am. 21:157-78, 2010. CrossRef
    158. Westbrook, B. K., and H. McKibben. Dance/movement therapy with groups of outpatients with Parkinson’s disease. / Am. J. Dance Ther. 11:27-8, 1989. CrossRef
    159. Wolpaw, J. R., N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan. Brain-computer interface technology: a review of the first international meeting. / IEEE Trans. Rehabil. Eng. 8:164-73, 2000. CrossRef
    160. Worth, P. F. How to treat Parkinson’s disease in 2013. / Clin. Med. 13:93-6, 2013. CrossRef
    161. Wu, A. D., F. Fregni, D. K. Simon, C. Deblieck, and A. Pascual-Leone. Noninvasive brain stimulation for Parkinson’s disease and dystonia. / Neurotherapeutics 5:345-61, 2008. CrossRef
    162. Yamamoto, T., Y. Katayama, J. Ushiba, H. Yoshino, T. Obuchi, K. Kobayashi, H. Oshima, and C. Fukaya. On-demand control system for deep brain stimulation for treatment of intention tremor. / Neuromodulation 16:230-35, 2013. CrossRef
    163. Yin, H. H., and B. J. Knowlton. The role of the basal ganglia in habit formation. / Nat. Rev. Neurosci. 7:464-76, 2006. CrossRef
    164. Zander, T. O., and C. Kothe. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. / J. Neural Eng. 8:025005, 2011. CrossRef
    165. Zhou, S., X. Chen, C. Wang, C. Yin, P. Hu, and K. Wang. Selective attention deficits in early and moderate stage Parkinson’s disease. / Neurosci. Lett. 509(1):50-5, 2012. CrossRef
  • 作者单位:Frédéric D. Broccard (1) (2)
    Tim Mullen (3)
    Yu Mike Chi (4)
    David Peterson (1) (5)
    John R. Iversen (3)
    Mike Arnold (6)
    Kenneth Kreutz-Delgado (3)
    Tzyy-Ping Jung (3)
    Scott Makeig (3)
    Howard Poizner (1)
    Terrence Sejnowski (1) (5) (7)
    Gert Cauwenberghs (1) (2)

    1. Institute for Neural Computation, University of California San Diego, La Jolla, CA, 92093, USA
    2. Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
    3. Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, 92093, USA
    4. Cognionics Inc., San Diego, CA, 92121, USA
    5. Computational Neuroscience Laboratory, Salk Institute for Biological Studies, La Jolla, CA, 92037, USA
    6. Isoloader USA Inc., 1745 Eolus Ave, Encinitas, CA, 92024-1519, USA
    7. Howard Hughes Medical Institute, 9500 Gilman Dr, La Jolla, CA, 92093, USA
  • ISSN:1573-9686
文摘
Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson’s disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.

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