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Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders

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Abstract

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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References

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 28:863–873, 2013.

    Article  PubMed Central  PubMed  Google Scholar 

  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 131:3348–3360, 2008.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Ashby, R. An Introduction to Cybernetics. London: Chapman & Hall, 1956.

    Google Scholar 

  5. Astrom, K. J., and B. Wittenmark. Adaptive Control (2nd ed.). Hoboken, New Jersey: Addison-Wesley, 1994.

    Google Scholar 

  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):302–307, 2001.

    Article  CAS  Google Scholar 

  7. Baram, Y. Walking on tiles. Neural Process. Lett. 10:81–87, 1999.

    Article  Google Scholar 

  8. Baram, Y., J. Aharon-Peretz, Y. Simionovici, and L. Ron. Walking on virtual tiles. Neural Process. Lett. 16:227–233, 2002.

    Article  Google Scholar 

  9. Baram, Y., and A. Miller. Virtual reality cues for improvement of gait in patients with multiple sclerosis. Neurology 66:178–181, 2006.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  11. Berns, G. S., and T. S. Sejnowski. A computational model of how the basal ganglia produce sequences. J. Cogn. Neurosci. 10:108–121, 1998.

    Article  CAS  PubMed  Google Scholar 

  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. 157–164.

  13. Boahen, K. A. Point-to-point connectivity between neuromorphic chips using address-events. IEEE Trans. Circuits Syst. II 47:416–434, 2000.

    Article  Google Scholar 

  14. Bogacz, R., and T. Larsen. Integration of reinforcement learning and optimal decision-making theories of the basal ganglia. Neural Comput. 23:817–851, 2011.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  16. Brittain, J. S., P. Robert-Smith, T. Z. Aziz, and P. Brown. Tremor suppression by rhythmic transcranial current stimulation. Curr. Biol. 23:436–440, 2013.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Carabalona, R., P. Castiglioni, and F. Gramatica. Brain-computer interfaces and neurorehabilitation. Stud. Health Technol. Inf. 145:160–176, 2009.

    Google Scholar 

  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.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  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’11), 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. 44:419–433, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Chakravarthy, V. S., D. Joseph, and R. S. Bapi. What do the basal ganglia do? A modeling perspective. Biol. Cybern. 103:237–253, 2010.

    Article  CAS  PubMed  Google Scholar 

  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):664–670, 1999.

    Article  CAS  PubMed  Google Scholar 

  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’10), Sevilla, Spain, September 13–17, 2010.

  24. Chi, Y. M., and G. Cauwenberghs. Wireless non-contact biopotential electrode. In: Proceedings Body Sensor Networks (BSN), BioPolis, Singapore, 7–9 June 2010.

  25. Chi, Y. M., T. P. Jung, and G. Cauwenberghs. Dry-contact and noncontact biopotential electrodes: methodological review. IEEE Rev. Biomed. Eng. 3:106–120, 2010.

    Article  PubMed  Google Scholar 

  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. 1:526–535, 2011.

    Article  Google Scholar 

  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. 20:228–235, 2012.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  29. Contreras-Vidal, J. L., and G. E. Stelmach. A neural model of basal ganglia-thalamocortical relations in normal and parkinsonian movement. Biol. Cybern. 73:467–476, 1995.

    Article  CAS  PubMed  Google Scholar 

  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. 12:2259–2278, 2000.

    Article  CAS  PubMed  Google Scholar 

  31. Daly, J. J., and J. R. Wolpaw. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 7:1032–1043, 2008.

    Article  PubMed  Google Scholar 

  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. 25:1693–1731, 2013.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  34. del R. Millán, J. Adaptive brain interfaces. Commun. ACM 46:75–80, 2003.

    Article  Google Scholar 

  35. Delbruck, T. Silicon retina with correlation-based, velocity-tuned pixels. IEEE Trans. Neural Netw. 4:529–541, 1993.

    Article  CAS  PubMed  Google Scholar 

  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 134:9–21, 2004.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 56:54–64, 2009.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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’11), 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. 24:357–363, 2009.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 55:322–334, 2008.

    Article  PubMed  Google Scholar 

  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. 47:573–582, 2010.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 23:265–282, 2007.

    Article  Google Scholar 

  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. 3:383–393, 2007.

    Article  PubMed  Google Scholar 

  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 6:1614–1623, 2005.

    Article  Google Scholar 

  51. Frucht, S. J. The definition of dystonia: current concepts and controversies. Mov. Disord. 28:884–888, 2013.

    Article  PubMed  Google Scholar 

  52. Ganguly, K., and J. M. Carmena. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 7:e1000153, 2009.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  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. 14:662–667, 2011.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):1330–1344, 2009.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 15:1752–1757, 2012.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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. 14:781–793, 2001.

    Article  CAS  PubMed  Google Scholar 

  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’13), Osaka, Japan, 3–7 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.

    Article  PubMed Central  PubMed  Google Scholar 

  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):593–608, 2011.

    PubMed  Google Scholar 

  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):212–224, 2004.

    Article  Google Scholar 

  62. Hale, K., and K. Stanney. Deriving haptic design guidelines from human physiological and neurological foundation. IEEE Comput. Graph. Appl. 24(2):39, 2004.

    Article  Google Scholar 

  63. Hasler, J., and B. Marr. Finding a roadmap to achieve large neuromorphic hardware systems. Front. Neurosci. 7:118, 2013.

    Article  PubMed Central  PubMed  Google Scholar 

  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 195:261–269, 2011.

    Article  PubMed Central  PubMed  Google Scholar 

  65. He, L., and C. Yang. Wilke, and H. Yuan. Electrophysiological imaging of brain activity and connectivity—challenges and opportunities. IEEE Trans. Biomed. Eng. 58:1918–1931, 2011.

    Article  PubMed Central  PubMed  Google Scholar 

  66. Hikosaka, O., and M. Isoda. Switching from automatic to controlled behavior: cortico-basal ganglia mechanisms. Trends Cogn. Sci. 14:154–161, 2010.

    Article  PubMed Central  PubMed  Google Scholar 

  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 442:164–171, 2005.

    Article  CAS  Google Scholar 

  68. Holden, M. K. Virtual environment for motor rehabilitation: review. CyberPsychol. Behav. 8(3):187–211, 2005.

    Article  PubMed  Google Scholar 

  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 444:56–60, 2006.

    Article  CAS  PubMed  Google Scholar 

  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. 105:19486–19491, 2008.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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):865–872, 2013.

    Article  CAS  PubMed  Google Scholar 

  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 483:331–335, 2012.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  76. Kothe, C. A., and S. Makeig. BCILAB: a platform for brain–computer interface development. J. Neural Eng. 10:056014, 2013.

    Article  PubMed  Google Scholar 

  77. Krebs, H. I., and N. Hogan. Robotic therapy: the tipping point. Am. J. Phys. Med. Rehabil. 91:S290–S297, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  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 15:7–20, 2003.

    Article  Google Scholar 

  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):111–121, 2008.

    Article  PubMed Central  PubMed  Google Scholar 

  80. Lebedev, M. A., and M. A. Nicolelis. Brain-machine interfaces: past, present and future. Trends Neurosci. 29(9):536–546, 2006.

    Article  CAS  PubMed  Google Scholar 

  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. 88:137–151, 2003.

    Article  PubMed  Google Scholar 

  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. 366:511–519, 2012.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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. 23:3162–3204, 2011.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 2012:1769–1772, 2012.

    PubMed  Google Scholar 

  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):7–13, 2013.

    Article  PubMed  Google Scholar 

  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. 1265:9–24, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 74:449–457, 2013.

    PubMed Central  PubMed  Google Scholar 

  89. Liu, C., and B. He. Noninvasive estimation of global activation sequence using the extended Kalman filter. IEEE Trans. Biomed. Eng. 58:541–549, 2011.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 20:720–729, 2012.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  93. Lotze, M., C. Braun, N. Birbaumer, S. Anders, and L. G. Cohen. Motor learning elicited by voluntary drive. Brain 126(4):866–872, 2003.

    Article  PubMed  Google Scholar 

  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 254:205–221, 2013.

    Article  CAS  PubMed  Google Scholar 

  95. Lyons, K. E., and R. Pahwa. Pharmacotherapy of essential tremor: an overview of existing and upcoming agents. CNS Drug 22:1037–1045, 2008.

    Article  CAS  Google Scholar 

  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. 26:461–481, 2003.

    Article  CAS  PubMed  Google Scholar 

  97. Mahmoudi, B., and J. C. Sanchez. A symbiotic brain–machine interface through value-based decision making. PLoS ONE 6:e14760, 2011.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  98. Makeig, S., K. Gramann, T.-P. Jung, T. J. Sejnowski, and H. Poizner. Linking brain, mind and behavior. Int. J. Psychophysiol. 73:95–100, 2009.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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 148:71–77, 2005.

    Article  PubMed  Google Scholar 

  101. Mead, C. Analog VLSI and Neural Systems. Hoboken, New Jersey: Addison-Wesley, 1989.

    Book  Google Scholar 

  102. Minogue, J., and M. G. Jones. Haptics in education: exploring an untapped sensory modality. Rev. Educ. Res. 76(3):3–17, 2006.

    Article  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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):1799–1809, 2010.

    Article  Google Scholar 

  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’13), Osaka, Japan, July 3–7, 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. 1115–1122.

  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 10:145–147, 2013.

    Article  CAS  PubMed  Google Scholar 

  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 71:607–610, 2001.

    Article  PubMed Central  PubMed  Google Scholar 

  109. Orsborn, A. L., and J. M. Carmena. Creating new functional circuits for action via brain–machine interfaces. Front. Comp. Neurosci. 7:157, 2013.

    Google Scholar 

  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. 2011:5436–5439, 2011.

    PubMed  Google Scholar 

  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. 20:468–477, 2012.

    Article  PubMed  Google Scholar 

  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. 129–136.

  113. Pascual-Leone, A., A. Amedi, F. Fregni, and L. B. Merabet. The plastic human brain cortex. Annu. Rev. Neurosci. 28:377–401, 2005.

    Article  CAS  PubMed  Google Scholar 

  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 81:589–598, 2013.

    Article  CAS  PubMed  Google Scholar 

  115. Peterson, D. A., T. J. Sejnowski, and H. Poizner. Convergent evidence for abnormal striatal synaptic plasticity in dystonia. Neurobiol. Dis. 37:558–573, 2010.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 12:716–726, 2013.

    Article  PubMed Central  PubMed  Google Scholar 

  117. Pizzolato, G., and T. Mandat. Deep brain stimulation for movement disorders. Front. Integr. Neurosci. 6:2, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  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. 590:3203–3212, 2012.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  119. Poor, H. V. An Introduction to Signal Detection and Estimation. New York: Springer, 1994.

    Book  Google Scholar 

  120. Priori, A., G. Foffani, L. Rossi, and S. Marceglia. Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp. Neurol. 245:77–86, 2013.

    Article  PubMed  Google Scholar 

  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. 7:63–75, 2012.

    Article  CAS  PubMed  Google Scholar 

  122. Rasch, M., N. K. Logothetis, and G. Kreiman. From neurons to circuits: linear estimation of local field potentials. J. Neurosci. 29(44):13785–13796, 2009.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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. 11:760–772, 2010.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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. 1479–1482, 2003.

  126. Reinkensmeyer, D. J., J. L. Emken, and S. C. Cramer. Robotics, motor learning, and neurologic recovery. Annu. Rev. Biomed. Eng. 6:497–525, 2004.

    Article  CAS  PubMed  Google Scholar 

  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 72:370–384, 2011.

    Article  CAS  PubMed  Google Scholar 

  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):263–306, 2004.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  130. Salmoni, S. Knowledge of results and motor learning. A review and critical reappraisal. Psychol. Bull. 95(3):355–386, 1984.

    Article  CAS  PubMed  Google Scholar 

  131. Sanchez, J. C., B. Mahmoudi, J. DiGiovanna, and J. C. Principe. Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants. Neural Netw. 22:305–315, 2009.

    Article  PubMed  Google Scholar 

  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. 19:15–24, 2011.

    Article  PubMed  Google Scholar 

  133. Schiff, S. J. Towards model-based control of Parkinson’s disease. Philos. Trans. A. Math. Phys. Eng. 368:2269–2308, 2010.

    Article  Google Scholar 

  134. Schmidt, R. A. Frequent augmented feedback can degrade learning: evidence and interpretations. Tutorials Motor Neurosci. 62:59–75, 1991.

    Article  Google Scholar 

  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. 20:1417–1438, 2009.

    Article  PubMed  Google Scholar 

  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. 1489–1496, 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. 20:21–53, 2013.

    Article  PubMed  Google Scholar 

  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. 7:713–721, 2013.

    Article  PubMed  Google Scholar 

  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. 33:15056–15068, 2013.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  140. Snijders, A. H., I. Toni, E. Ruzicka, and B. R. Bloem. Bicycling breaks the ice for freezers of gait. Mov. Disord. 26(3):367–371, 2011.

    Article  PubMed  Google Scholar 

  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. 20:410–421, 2012.

    Article  PubMed  Google Scholar 

  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):255–261, 2007.

    Article  PubMed  Google Scholar 

  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):16777–16787, 2010.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  144. Suminski, A. J., D. C. Tkach, and N. G. Hatsopoulos. Exploiting multiple sensory modalities in brain–machine interfaces. Neural Netw. 22:1224–1234, 2009.

    Article  PubMed Central  PubMed  Google Scholar 

  145. Sutton, R. S., and A. G. Barto. Reinforcement Learning: An introduction. Cambridge, MA: MIT Press, 1998.

    Google Scholar 

  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. 31:5721–5729, 2011.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  147. Taylor, D. M., S. I. Tillery, and A. B. Schwartz. Direct cortical control of 3D neuroprosthetic devices. Science 296:1829–1832, 2002.

    Article  CAS  PubMed  Google Scholar 

  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):387–416, 2011.

    Article  PubMed  Google Scholar 

  149. Thenganatt, M. A., and S. Fahn. Botulinum toxin for the treatment of movement disorders. Curr. Neurol. Neurosci. 12:399–409, 2012.

    Article  CAS  Google Scholar 

  150. Torres, E. B., K. M. Heilman, and H. Poizner. Impaired endogenously evoked automated reaching in Parkinson’s disease. J. Neurosci. 31:17848–17863, 2011.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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. 121:1109–1120, 2010.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  152. Tubiana, R. Musician’s focal dystonia. Hand Clin. 19:303–308, 2003.

    Article  PubMed  Google Scholar 

  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. 13:425–433, 2007.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):77–85, 2011.

    Article  PubMed  Google Scholar 

  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. 19:2281–2300, 2007.

    Article  PubMed  Google Scholar 

  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. 18:253–265, 2007.

    Article  PubMed  Google Scholar 

  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. 21:157–178, 2010.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  158. Westbrook, B. K., and H. McKibben. Dance/movement therapy with groups of outpatients with Parkinson’s disease. Am. J. Dance Ther. 11:27–38, 1989.

    Article  Google Scholar 

  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. 8:164–173, 2000.

    Article  CAS  PubMed  Google Scholar 

  160. Worth, P. F. How to treat Parkinson’s disease in 2013. Clin. Med. 13:93–96, 2013.

    Article  PubMed  Google Scholar 

  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 5:345–361, 2008.

    Article  PubMed Central  PubMed  Google Scholar 

  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 16:230–235, 2013.

    Article  PubMed  Google Scholar 

  163. Yin, H. H., and B. J. Knowlton. The role of the basal ganglia in habit formation. Nat. Rev. Neurosci. 7:464–476, 2006.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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):50–55, 2012.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The authors acknowledge support from National Science Foundation grant EFRI-1137279 (M3C: Mind, Machines, and Motor Control). DP would like to acknowledge support from the Bachmann-Strauss Dystonia & Parkinson’s Foundation, the Benign Essential Blepharospasm Research Foundation, the dystonia Coalition (NS065701), the Kavli Institute for Brain and Mind and a grant from the NSF to the Temporal Dynamics of Learning Center (SBE-0542013). HP is supported by the NSF grant #SMA-1041755 and the ONR MURI Award No.: N00014-10-1-0072. SM would like to acknowledge a gift from The Swartz Foundation (Old Field NY) and the NINDS grant R01-NS047293-09A1. The authors would like to recognize the contributions of Alejandro Ojeda Gonzalez for designing the MoBILAB environment and Christian Kothe for designing the data collection system LSL and the BCILAB extension for the MoBI setup. The authors would like to thank Nikil Govil and Abraham Akinin for carrying out preliminary experiments on proprioception with Parkinson’s disease patients and Trevor Kerth from Cognionics for help and assistance during data collection with the 64-channel dry EEG headset. The authors also would like to thank all the participants at the 2012 IEEE EMB/CAS/SMC workshop on Brain–Machine–Body Interfaces in San Diego, as well as the participants at the 2012 NSF EFRI Grantees Conference in Washington DC, for insightful interactions and discussions.

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Broccard, F.D., Mullen, T., Chi, Y.M. et al. Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders. Ann Biomed Eng 42, 1573–1593 (2014). https://doi.org/10.1007/s10439-014-1032-6

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