Combining decoder design and neural adaptation in brain-machine interfaces

Neuron

Volumn 84, Issue 4, 2014, Pages 665-680

  Shenoy, KrishnaV ^a   Carmena, JoseM ^b  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARM MOVEMENT; BIOMIMETICS; BRAIN COMPUTER INTERFACE; CONTROL STRATEGY; DECODER; GENERAL DEVICE; HUMAN; MOTOR PERFORMANCE; NERVE FUNCTION; NERVOUS SYSTEM FUNCTION; NEURAL ADAPTATION; NEUROMODULATION; NONHUMAN; PROCESS DESIGN; PROSTHESIS; REVIEW; ROBOTICS; TASK PERFORMANCE; BRAIN; MOTOR CORTEX; MOVEMENT (PHYSIOLOGY); NERVE CELL; PHYSIOLOGY;

BRAIN; BRAIN-COMPUTER INTERFACES; HUMANS; MOTOR CORTEX; MOVEMENT; NEURONS;

EID: 84918842843     PISSN: 08966273     EISSN: 10974199     Source Type: Journal    
DOI: 10.1016/j.neuron.2014.08.038     Document Type: Review

References (106)

1. Achtman N., Afshar A., Santhanam G., Yu B.M., Ryu S.I., Shenoy K.V. Free-paced high-performance brain-computer interfaces. J.Neural Eng. 2007, 4:336-347.
2. Anderson K.D. Targeting recovery: priorities of the spinal cord-injured population. J.Neurotrauma 2004, 21:1371-1383.
3. Baranauskas G. What limits the performance of current invasive brain machine interfaces?. Front. Syst. Neurosci. 2014, 8:68.
4. Bensmaia S.J., Miller L.E. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat. Rev. Neurosci. 2014, 15:313-325.
5. Bishop W., Chestek C.C., Gilja V., Nuyujukian P., Foster J.D., Ryu S.I., Shenoy K.V., Yu B.M. Self-recalibrating classifiers for intracortical brain-computer interfaces. J.Neural Eng. 2014, 11:026001.
6. Borton D., Micera S., Millan J.R., Courtine G. Personalized neuroprosthetics. Sci. Transl. Med. 2013, 5:210rv2.
7. Card S.K., English W.K., Burr B.J. Evaluation of mouse, rate-controlled isometric joystick, step keys, and text keys for text selection on a CRT. Ergonomics 1978, 21:601-613.
8. Carmena J.M. Advances in neuroprosthetic learning and control. PLoS Biol. 2013, 11:e1001561.
9. Carmena J.M., Lebedev M.A., Crist R.E., O'Doherty J.E., Santucci D.M., Dimitrov D.F., Patil P.G., Henriquez C.S., Nicolelis M.A. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 2003, 1:E42.
10. Chadwick E.K., Blana D., Simeral J.D., Lambrecht J., Kim S.P., Cornwell A.S., Taylor D.M., Hochberg L.R., Donoghue J.P., Kirsch R.F. Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia. J.Neural Eng. 2011, 8:034003.
11. Chase S.M., Schwartz A.B., Kass R.E. Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms. Neural Netw. 2009, 22:1203-1213.
12. Chase S.M., Kass R.E., Schwartz A.B. Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J.Neurophysiol. 2012, 108:624-644.
13. Chestek C.A., Gilja V., Nuyujukian P., Foster J.D., Fan J.M., Kaufman M.T., Churchland M.M., Rivera-Alvidrez Z., Cunningham J.P., Ryu S.I., Shenoy K.V. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J.Neural Eng. 2011, 8:045005.
14. Churchland M.M., Cunningham J.P., Kaufman M.T., Foster J.D., Nuyujukian P., Ryu S.I., Shenoy K.V. Neural population dynamics during reaching. Nature 2012, 487:51-56.
15. Clancy K.B., Koralek A.C., Costa R.M., Feldman D.E., Carmena J.M. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 2014, 17:807-809.
16. Collinger J.L., Wodlinger B., Downey J.E., Wang W., Tyler-Kabara E.C., Weber D.J., McMorland A.J., Velliste M., Boninger M.L., Schwartz A.B. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 2013, 381:557-564.
17. Cunningham J.P., Nuyujukian P., Gilja V., Chestek C.A., Ryu S.I., Shenoy K.V. A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. J.Neurophysiol. 2011, 105:1932-1949.
18. Dangi S., Orsborn A.L., Moorman H.G., Carmena J.M. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces. Neural Comput. 2013, 25:1693-1731.
19. Dangi S., Gowda S., Moorman H.G., Orsborn A.L., So K., Shanechi M., Carmena J.M. Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Comput. 2014, 26:1811-1839.
20. DiGiovanna J., Mahmoudi B., Fortes J., Principe J.C., Sanchez J.C. Coadaptive brain-machine interface via reinforcement learning. IEEE Trans. Biomed. Eng. 2009, 56:54-64.
21. Durand D.M., Ghovanloo M., Krames E. Time to address the problems at the neural interface. J.Neural Eng. 2014, 11:020201.
22. Ethier C., Oby E.R., Bauman M.J., Miller L.E. Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 2012, 485:368-371.
23. Fagg A.H., Hatsopoulos N.G., de Lafuente V., Moxon K.A., Nemati S., Rebesco J.M., Romo R., Solla S.A., Reimer J., Tkach D., et al. Biomimetic brain machine interfaces for the control of movement. J.Neurosci. 2007, 27:11842-11846.
24. Fan J.M., Nuyujukian P., Kao J.C., Chestek C.A., Ryu S.I., Shenoy K.V. Intention estimation in brain-machine interfaces. J.Neural Eng. 2014, 11:016004.
25. Fetz E.E. Operant conditioning of cortical unit activity. Science 1969, 163:955-958.
26. Fetz E.E. Volitional control of neural activity: implications for brain-computer interfaces. J.Physiol. 2007, 579:571-579.
27. Gage G., Ludwig K., Otto K., Ionides E., Kipke D. Naive coadaptive cortical control. J.Neural Eng. 2005, 2:52-63.
28. Ganguly K., Carmena J.M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 2009, 7:e1000153.
29. Ganguly K., Carmena J.M. Neural correlates of skill acquisition with a cortical brain-machine interface. J.Mot. Behav. 2010, 42:355-360.
30. Ganguly K., Secundo L., Ranade G., Orsborn A., Chang E.F., Dimitrov D.F., Wallis J.D., Barbaro N.M., Knight R.T., Carmena J.M. Corticalrepresentation of ipsilateral arm movements in monkey and man. J.Neurosci. 2009, 29:12948-12956.
31. Ganguly K., Dimitrov D.F., Wallis J.D., Carmena J.M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 2011, 14:662-667.
32. Gilja V., Chestek C.A., Diester I., Henderson J.M., Deisseroth K., Shenoy K.V. Challenges and opportunities for next-generation intracortically based neural prostheses. IEEE Trans. Biomed. Eng. 2011, 58:1891-1899.
33. Gilja V., Nuyujukian P., Chestek C.A., Cunningham J.P., Yu B.M., Fan J.M., Churchland M.M., Kaufman M.T., Kao J.C., Ryu S.I., Shenoy K.V. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 2012, 15:1752-1757.
34. Gowda S., Orsborn A.L., Overduin S.A., Moorman H.G., Carmena J.M. Designing dynamical properties of brain-machine interfaces to optimize task-specific performance. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 22:911-920.
35. Green A.M., Kalaska J.F. Learning to move machines with the mind. Trends Neurosci. 2011, 34:61-75.
36. Gulati T., Ramanathan D.S., Wong C.C., Ganguly K. Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning. Nat. Neurosci. 2014, 17:1107-1113.
37. Hauschild M., Mulliken G.H., Fineman I., Loeb G.E., Andersen R.A. Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands. Proc. Natl. Acad. Sci. USA 2012, 109:17075-17080.
38. Heliot R., Venkatraman S., Carmena J.M. Decoder remapping to counteract neuron loss in brain-machine interfaces. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010, 1670-1673.
39. Heliot R., Ganguly K., Jimenez J., Carmena J.M. Learning in closed-loop brain-machine interfaces: modeling and experimental validation. IEEE Trans. Syst. Man. Cybern. B Cybern. 2010, 40:1387-1397.
40. Hochberg L.R., Serruya M.D., Friehs G.M., Mukand J.A., Saleh M., Caplan A.H., Branner A., Chen D., Penn R.D., Donoghue J.P. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006, 442:164-171.
41. Hochberg L.R., Bacher D., Jarosiewicz B., Masse N.Y., Simeral J.D., Vogel J., Haddadin S., Liu J., Cash S.S., van der Smagt P., Donoghue J.P. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 2012, 485:372-375.
42. Homer M.L., Nurmikko A.V., Donoghue J.P., Hochberg L.R. Sensors and decoding for intracortical brain computer interfaces. Annu. Rev. Biomed. Eng. 2013, 15:383-405.
43. Hwang E.J., Bailey P.M., Andersen R.A. Volitional control of neural activity relies on the natural motor repertoire. Curr. Biol. 2013, 23:353-361.
44. Ifft P.J., Shokur S., Li Z., Lebedev M.A., Nicolelis M.A. A brain-machine interface enables bimanual arm movements in monkeys. Sci. Transl. Med. 2013, 5:210ra154.
45. Jackson A., Fetz E.E. Interfacing with the computational brain. IEEE Trans. Neural Syst. Rehabil. Eng. 2011, 19:534-541.
46. Jackson A., Mavoori J., Fetz E.E. Long-term motor cortex plasticity induced by an electronic neural implant. Nature 2006, 444:56-60.
47. Jarosiewicz B., Chase S.M., Fraser G.W., Velliste M., Kass R.E., Schwartz A.B. Functional network reorganization during learning in a brain-computer interface paradigm. Proc. Natl. Acad. Sci. USA 2008, 105:19486-19491.
48. Jarosiewicz B., Masse N.Y., Bacher D., Cash S.S., Eskandar E., Friehs G., Donoghue J.P., Hochberg L.R. Advantages of closed-loop calibration in intracortical brain-computer interfaces for people with tetraplegia. J.Neural Eng. 2013, 10:046012.
49. Judy J.W. Neural interfaces for upper-limb prosthesis control: opportunities to improve long-term reliability. IEEE Pulse 2012, 3:57-60.
50. Kao J.C., Nuyujukian P., Cunningham J.P., Churchland M.M., Ryu S.I., Shenoy K.V. Increasing brain-machine interface performance by modeling neural population dynamics. Program No. 653.05. 2013 Neuroscience Meeting Planner 2013, Society for Neuroscience. Online. San Diego, CA, Washington, DC.
51. Kao J.C., Stavisky S., Sussillo D., Nuyujukian P., Shenoy K.V. Information systems opportunities in brain-machine interface decoders. Proceedings of the IEEE 2014, 102:666-682.
52. Kaufman M.T., Churchland M.M., Ryu S.I., Shenoy K.V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 2014, 17:440-448.
53. Kennedy P.R., Bakay R.A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 1998, 9:1707-1711.
54. Kennedy P.R., Bakay R.A., Moore M.M., Adams K., Goldwaithe J. Direct control of a computer from the human central nervous system. IEEE Trans. Rehabil. Eng. 2000, 8:198-202.
55. Kennedy P.R., Kirby M.T., Moore M.M., King B., Mallory A. Computer control using human intracortical local field potentials. IEEE Trans. Neural Syst. Rehabil. Eng. 2004, 12:339-344.
56. Kim S.P., Simeral J.D., Hochberg L.R., Donoghue J.P., Black M.J. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J.Neural Eng. 2008, 5:455-476.
57. Kim S.P., Simeral J.D., Hochberg L.R., Donoghue J.P., Friehs G.M., Black M.J. Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia. IEEE Trans. Neural Syst. Rehabil. Eng. 2011, 19:193-203.
58. Koralek A.C., Jin X., Long J.D., Costa R.M., Carmena J.M. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 2012, 483:331-335.
59. Koralek A.C., Costa R.M., Carmena J.M. Temporally precise cell-specific coherence develops in corticostriatal networks during learning. Neuron 2013, 79:865-872.
60. Koyama S., Chase S.M., Whitford A.S., Velliste M., Schwartz A.B., Kass R.E. Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control. J.Comput. Neurosci. 2010, 29:73-87.
61. Lebedev M.A., Carmena J.M., O'Doherty J.E., Zacksenhouse M., Henriquez C.S., Principe J.C., Nicolelis M.A. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J.Neurosci. 2005, 25:4681-4693.
62. Legenstein R., Chase S.M., Schwartz A.B., Maass W. A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task. J.Neurosci. 2010, 30:8400-8410.
63. Li Z., O'Doherty J.E., Lebedev M.A., Nicolelis M.A. Adaptive decoding for brain-machine interfaces through Bayesian parameter updates. Neural Comput. 2011, 23:3162-3204.
64. Mahmoudi B., Sanchez J.C. A symbiotic brain-machine interface through value-based decision making. PLoS ONE 2011, 6:e14760.
65. Merel J., Fox R., Jebara T., Paninski L. A multi-agent control framework for co-adaptation in brain-computer interfaces. Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2013, 2841-2849.
66. Millán Jdel.R., Carmena J.M. Invasive or noninvasive: understanding brain-machine interface technology. IEEE Eng. Med. Biol. Mag. 2010, 29:16-22.
67. Moran D.W. Evolution of brain-computer interface: action potentials, local field potentials and electrocorticograms. Curr. Opin. Neurobiol. 2010, 20:741-745.
68. Moritz C.T., Perlmutter S.I., Fetz E.E. Direct control of paralysed muscles by cortical neurons. Nature 2008, 456:639-642.
69. Mulliken G.H., Musallam S., Andersen R.A. Decoding trajectories from posterior parietal cortex ensembles. J.Neurosci. 2008, 28:12913-12926.
70. Musallam S., Corneil B.D., Greger B., Scherberger H., Andersen R.A. Cognitive control signals for neural prosthetics. Science 2004, 305:258-262.
71. Nuyujukian P., Fan J.M., Gilja V., Kalanithi P.S., Chestek C.A., Shenoy K.V. Monkey models for brain-machine interfaces: the need for maintaining diversity. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 1301-1305.
72. Nuyujukian P., Fan J.M., Kao J.C., Ryu S.I., Shenoy K.V. A high-performance keyboard neural prosthesis enabled by task optimization. IEEE Trans. Biomed. Eng 2014, Published online September 4, 2014. 10.1109/TBME.2014.2354697.
73. Nuyujukian P., Kao J.C., Fan J.M., Stavisky S.D., Ryu S.I., Shenoy K.V. Performance sustaining intracortical neural prostheses. J.Neural Eng. 2014, Published online October 13, 2014. 10.1088/1741-2560/11/6/066003.
74. O'Doherty J.E., Lebedev M.A., Ifft P.J., Zhuang K.Z., Shokur S., Bleuler H., Nicolelis M.A. Active tactile exploration using a brain-machine-brain interface. Nature 2011, 479:228-231.
75. Orsborn A.L., Carmena J.M. Creating new functional circuits for action via brain-machine interfaces. Front. Comput. Neurosci. 2013, 7:157.
76. Orsborn A.L., Dangi S., Moorman H.G., Carmena J.M. Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions. IEEE Trans. Neural Syst. Rehabil. Eng. 2012, 20:468-477.
77. Orsborn A.L., Moorman H.G., Overduin S.A., Shanechi M.M., Dimitrov D.F., Carmena J.M. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 2014, 82:1380-1393.
78. Pohlmeyer E.A., Oby E.R., Perreault E.J., Solla S.A., Kilgore K.L., Kirsch R.F., Miller L.E. Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles. PLoS ONE 2009, 4:e5924.
79. Radhakrishnan S.M., Baker S.N., Jackson A. Learning a novel myoelectric-controlled interface task. J.Neurophysiol. 2008, 100:2397-2408.
80. Ryu S.I., Shenoy K.V. Human cortical prostheses: lost in translation?. Neurosurg. Focus 2009, 27:E5.
81. Sadtler P.T., Quick K.M., Golub M.D., Chase S.M., Ryu S.I., Tyler-Kabara E.C., Yu B.M., Batista A.P. Neural constraints on learning. Nature 2014, 512:423-426.
82. Santhanam G., Ryu S.I., Yu B.M., Afshar A., Shenoy K.V. A high-performance brain-computer interface. Nature 2006, 442:195-198.
83. Santhanam G., Yu B.M., Gilja V., Ryu S.I., Afshar A., Sahani M., Shenoy K.V. Factor-analysis methods for higher-performance neural prostheses. J.Neurophysiol. 2009, 102:1315-1330.
84. Serruya M.D., Hatsopoulos N.G., Paninski L., Fellows M.R., Donoghue J.P. Instant neural control of a movement signal. Nature 2002, 416:141-142.
85. Shanechi, M.M., Orsborn, A., Gowda, S., and Carmena, J.M. (2013). Proficient BMI control enabled by closed-loop adaptation of an optimal feedback-controlled point process decoder. Proceedings of Translational and Computational Motor Control (San Diego, CA: TCMC).
86. Shenoy K.V., Meeker D., Cao S., Kureshi S.A., Pesaran B., Buneo C.A., Batista A.P., Mitra P.P., Burdick J.W., Andersen R.A. Neural prosthetic control signals from plan activity. Neuroreport 2003, 14:591-596.
87. Shenoy K.V., Kaufman M.T., Sahani M., Churchland M.M. Adynamical systems view of motor preparation: implications for neural prosthetic system design. Prog. Brain Res. 2011, 192:33-58.
88. Shenoy K.V., Sahani M., Churchland M.M. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 2013, 36:337-359.
89. Shmuelof L., Krakauer J.W., Mazzoni P. How is a motor skill learned? Change and invariance at the levels of task success and trajectory control. J.Neurophysiol. 2012, 108:578-594.
90. Shpigelman, L., Lalazar, H., and Vaadia, E. (2009). Kernel-ARMA for hand tracking and brain-machine interfacing during 3D motor control. D. Koller, D.Schuurmans, Y. Bengio, L. Bottou, eds. Advances in Neural Information Processing Systems 21, 1489-1496.
91. Simeral J.D., Kim S.-P., Black M.J., Donoghue J.P., Hochberg L.R. Neural control of cursor trajectory and click by a human with tetraplegia 1,000days after implant of an intracortical microelectrode array. J.Neural Eng. 2011, 8:025027.
92. So K., Ganguly K., Jimenez J., Gastpar M.C., Carmena J.M. Redundant information encoding in primary motor cortex during natural and prosthetic motor control. J.Comput. Neurosci. 2012, 32:555-561.
93. Stanley J., Krakauer J.W. Motor skill depends on knowledge of facts. Front. Hum. Neurosci. 2013, 7:503.
94. Suminski A.J., Tkach D.C., Fagg A.H., Hatsopoulos N.G. Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J.Neurosci. 2010, 30:16777-16787.
95. Sussillo D., Nuyujukian P., Fan J.M., Kao J.C., Stavisky S.D., Ryu S.I., Shenoy K.V. A recurrent neural network for closed-loop intracortical brain-machine interface decoders. J.Neural Eng. 2012, 9:026027.
96. Taylor J.A., Ivry R.B. The role of strategies in motor learning. Ann. N Y Acad. Sci. 2012, 1251:1-12.
97. Taylor D.M., Tillery S.I., Schwartz A.B. Direct cortical control of 3D neuroprosthetic devices. Science 2002, 296:1829-1832.
98. Tkach D., Reimer J., Hatsopoulos N.G. Observation-based learning for brain-machine interfaces. Curr. Opin. Neurobiol. 2008, 18:589-594.
99. Velliste M., Perel S., Spalding M.C., Whitford A.S., Schwartz A.B. Cortical control of a prosthetic arm for self-feeding. Nature 2008, 453:1098-1101.
100. Venkatraman S., Carmena J.M. Active sensing of target location encoded by cortical microstimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 2011, 19:317-324.
101. Wahnoun R., He J., Helms Tillery S.I. Selection and parameterization of cortical neurons for neuroprosthetic control. J.Neural Eng. 2006, 3:162-171.
102. Wander J.D., Blakely T., Miller K.J., Weaver K.E., Johnson L.A., Olson J.D., Fetz E.E., Rao R.P.N., Ojemann J.G. Distributed cortical adaptation during learning of a brain-computer interface task. Proc. Natl. Acad. Sci. USA 2013, 110:10818-10823.
103. Wang W., Collinger J.L., Perez M.A., Tyler-Kabara E.C., Cohen L.G., Birbaumer N., Brose S.W., Schwartz A.B., Boninger M.L., Weber D.J. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. Phys. Med. Rehabil. Clin. N. Am. 2010, 21:157-178.
104. Wessberg J., Stambaugh C.R., Kralik J.D., Beck P.D., Laubach M., Chapin J.K., Kim J., Biggs S.J., Srinivasan M.A., Nicolelis M.A. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 2000, 408:361-365.
105. Wu W., Shaikhouni A., Donoghue J.P., Black M.J. Closed-loop neural control of cursor motion using a kalman filter. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2004, 6:4126-4129.
106. Yin H.H., Mulcare S.P., Hilário M.R., Clouse E., Holloway T., Davis M.I., Hansson A.C., Lovinger D.M., Costa R.M. Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill. Nat. Neurosci. 2009, 12:333-341.