Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System

Literature Cited

1. 1. 

   Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M et al. 2006. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442:164–71
   [Google Scholar]
2. 2. 

   Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD et al. 2012. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485:372–75
   [Google Scholar]
3. 3. 

   Vogel J, Haddadin S, Jarosiewicz B, Simeral J, Bacher D et al. 2015. An assistive decision-and-control architecture for force-sensitive hand–arm systems driven by human–machine interfaces. Int. J. Robot. Res. 34:763–80
   [Google Scholar]
4. 4. 

   Wodlinger B, Downey JE, Tyler-Kabara EC, Schwartz AB, Boninger ML, Collinger JL. 2015. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations. J. Neural Eng. 12:016011
   [Google Scholar]
5. 5. 

   Downey JE, Weiss JM, Muelling K, Venkatraman A, Valois JS et al. 2016. Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. J. NeuroEng. Rehabil. 13:28
   [Google Scholar]
6. 6. 

   Nuyujukian P, Albites Sanabria J, Saab J, Pandarinath C, Jarosiewicz B et al. 2018. Cortical control of a tablet computer by people with paralysis. PLOS ONE 13:e0204566
   [Google Scholar]
7. 7. 

   Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV. 2020. High-performance brain-to-text communication via imagined handwriting. bioRxiv 2020.07.01.183384. https://doi.org/10.1101/2020.07.01.183384
   [Crossref]
8. 8. 

   Fetz EE. 1969. Operant conditioning of cortical unit activity. Science 163:955–58
   [Google Scholar]
9. 9. 

   Pandarinath C, Nuyujukian P, Blabe CH, Sorice BL, Saab J et al. 2017. High performance communication by people with paralysis using an intracortical brain-computer interface. eLife 6:e18554
   [Google Scholar]
10. 10. 

    Jun JJ, Steinmetz NA, Siegle JH, Denman DJ, Bauza M et al. 2017. Fully integrated silicon probes for high-density recording of neural activity. Nature 551:232–36
    [Google Scholar]
11. 11. 

    Georgopoulos A, Schwartz A, Kettner R. 1986. Neuronal population coding of movement direction. Science 233:1416–19
    [Google Scholar]
12. 12. 

    Truccolo W, Friehs GM, Donoghue JP, Hochberg LR. 2008. Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. J. Neurosci. 28:1163–78
    [Google Scholar]
13. 13. 

    Gilja V, Pandarinath C, Blabe CH, Nuyujukian P, Simeral JD et al. 2015. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21:1142–45
    [Google Scholar]
14. 14. 

    Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP. 2004. Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J. Neurophysiol. 91:515–32
    [Google Scholar]
15. 15. 

    Bullard AJ, Nason SR, Irwin ZT, Nu CS, Smith B et al. 2019. Design and testing of a 96-channel neural interface module for the Networked Neuroprosthesis system. Bioelectron. Med. 5:3
    [Google Scholar]
16. 16. 

    Hatsopoulos NG, Xu Q, Amit Y. 2007. Encoding of movement fragments in the motor cortex. J. Neurosci. 27:5105–14
    [Google Scholar]
17. 17. 

    Kadmon Harpaz N, Ungarish D, Hatsopoulos NG, Flash T 2019. Movement decomposition in the primary motor cortex. Cereb. Cortex 29:1619–33
    [Google Scholar]
18. 18. 

    Todorov E 2003. On the role of primary motor cortex in arm movement control. Progress in Motor Control, Vol. 3: Effects of Age, Disorder, and Rehabilitation ML Latash, MF Levin 125–66 Champaign, IL: Hum. Kinet.
    [Google Scholar]
19. 19. 

    Hatsopoulos N, Mukand J, Polykoff G, Friehs G, Donoghue J. 2005. Cortically controlled brain-machine interface. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference7660–63 Piscataway, NJ: IEEE
    [Google Scholar]
20. 20. 

    Vargas-Irwin CE, Franquemont L, Black MJ, Donoghue JP. 2015. Linking objects to actions: encoding of target object and grasping strategy in primate ventral premotor cortex. J. Neurosci. 35:10888–97
    [Google Scholar]
21. 21. 

    Achtman N, Afshar A, Santhanam G, Yu BM, Ryu SI, Shenoy KV. 2007. Free-paced high-performance brain–computer interfaces. J. Neural Eng. 4:336–47
    [Google Scholar]
22. 22. 

    Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. 2015. A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes. J. Neural Eng. 12:036009
    [Google Scholar]
23. 23. 

    Athalye VR, Ganguly K, Costa RM, Carmena JM. 2017. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron 93:955–970.e5
    [Google Scholar]
24. 24. 

    Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV. 2006. A high-performance brain–computer interface. Nature 442:195–98
    [Google Scholar]
25. 25. 

    Andersen R, Musallam S, Burdick J, Cham J. 2005. Cognitive based neural prosthetics. Proceedings of the 2005 IEEE International Conference on Robotics and Automation1908–13 Piscataway, NJ: IEEE
    [Google Scholar]
26. 26. 

    Buneo CA, Andersen RA. 2006. The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. Neuropsychologia 44:2594–606
    [Google Scholar]
27. 27. 

    Calton JL, Taube JS. 2009. Where am I and how will I get there from here? A role for posterior parietal cortex in the integration of spatial information and route planning. Neurobiol. Learn. Mem. 91:186–96
    [Google Scholar]
28. 28. 

    Minderer M, Brown KD, Harvey CD. 2019. The spatial structure of neural encoding in mouse posterior cortex during navigation. Neuron 102:232–48.e11
    [Google Scholar]
29. 29. 

    Harvey CD, Coen P, Tank DW. 2012. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484:62–68
    [Google Scholar]
30. 30. 

    Snyder LH, Batista AP, Andersen RA. 1997. Coding of intention in the posterior parietal cortex. Nature 386:167–70
    [Google Scholar]
31. 31. 

    Cui H. 2016. Forward prediction in the posterior parietal cortex and dynamic brain-machine interface. Front. Integr. Neurosci. 10:35
    [Google Scholar]
32. 32. 

    Bosco A, Breveglieri R, Filippini M, Galletti C, Fattori P. 2019. Reduced neural representation of arm/hand actions in the medial posterior parietal cortex. Sci. Rep. 9:936
    [Google Scholar]
33. 33. 

    Filippini M, Breveglieri R, Akhras MA, Bosco A, Chinellato E, Fattori P. 2017. Decoding information for grasping from the macaque dorsomedial visual stream. J. Neurosci. 37:4311–22
    [Google Scholar]
34. 34. 

    Hwang E, Bailey P, Andersen R. 2013. Volitional control of neural activity relies on the natural motor repertoire. Curr. Biol. 23:353–61
    [Google Scholar]
35. 35. 

    Clancy KB, Mrsic-Flogel TD 2019. The sensory representation of causally controlled objects. bioRxiv 786467. https://doi.org/10.1101/786467
    [Crossref]
36. 36. 

    Aflalo T, Kellis S, Klaes C, Lee B, Shi Y et al. 2015. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348:906–10
    [Google Scholar]
37. 37. 

    Philip BA, Rao N, Donoghue JP. 2013. Simultaneous reconstruction of continuous hand movements from primary motor and posterior parietal cortex. Exp. Brain Res. 225:361–75
    [Google Scholar]
38. 38. 

    Tampuu A, Matiisen T, Ólafsdóttir HF, Barry C, Vicente R. 2019. Efficient neural decoding of self-location with a deep recurrent network. PLOS Comput. Biol. 15:e1006822
    [Google Scholar]
39. 39. 

    Tu M, Zhao R, Adler A, Gan WB, Chen ZS. 2020. Efficient position decoding methods based on fluorescence calcium imaging in the mouse hippocampus. Neural Comput 32:1144–67
    [Google Scholar]
40. 40. 

    O'Keefe J, Dostrovsky J. 1971. The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat. Brain Res 34:171–75
    [Google Scholar]
41. 41. 

    Garvert MM, Dolan RJ, Behrens TE. 2017. A map of abstract relational knowledge in the human hippocampal–entorhinal cortex. eLife 6:e17086
    [Google Scholar]
42. 42. 

    Schevon CA, Tobochnik S, Eissa T, Merricks E, Gill B et al. 2019. Multiscale recordings reveal the dynamic spatial structure of human seizures. Neurobiol. Dis. 127:303–11
    [Google Scholar]
43. 43. 

    Patel KV, Katz CN, Kalia SK, Popovic MR, Valiante TA. 2020. Volitional control of individual neurons in the human brain. bioRxiv 2020.05.05.079038. https://doi.org/10.1101/2020.05.05.079038
    [Crossref]
44. 44. 

    Chung JE, Joo HR, Fan JL, Liu DF, Barnett AH et al. 2019. High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays. Neuron 101:21–31
    [Google Scholar]
45. 45. 

    Musk E, Neuralink 2019. An integrated brain-machine interface platform with thousands of channels. J. Med. Internet Res. 21:e16194
    [Google Scholar]
46. 46. 

    Chiang CH, Won SM, Orsborn AL, Yu KJ, Trumpis M et al. 2020. Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates. Sci. Transl. Med. 12:eaay4682
    [Google Scholar]
47. 47. 

    Simeral JD, Hosman T, Saab J, Flesher SN, Vilela M et al. 2019. Home use of a wireless intracortical brain-computer interface by individuals with tetraplegia. medRxiv 2019.12.27.19015727. https://doi.org/10.1101/2019.12.27.19015727
    [Crossref]
48. 48. 

    Perge JA, Homer ML, Malik WQ, Cash S, Eskandar E et al. 2013. Intra-day signal instabilities affect decoding performance in an intracortical neural interface system. J. Neural Eng. 10:036004
    [Google Scholar]
49. 49. 

    Downey JE, Schwed N, Chase SM, Schwartz AB, Collinger JL. 2018. Intracortical recording stability in human brain–computer interface users. J. Neural Eng. 15:046016
    [Google Scholar]
50. 50. 

    Perel S, Sadtler PT, Oby ER, Ryu SI, Tyler-Kabara EC et al. 2015. Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics. J. Neurophysiol. 114:1500–12
    [Google Scholar]
51. 51. 

    Christie BP, Tat DM, Irwin ZT, Gilja V, Nuyujukian P et al. 2015. Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain–machine interface performance. J. Neural Eng. 12:016009
    [Google Scholar]
52. 52. 

    Flint RD, Scheid MR, Wright ZA, Solla SA, Slutzky MW. 2016. Long-term stability of motor cortical activity: implications for brain machine interfaces and optimal feedback control. J. Neurosci. 36:3623–32
    [Google Scholar]
53. 53. 

    Jackson A, Hall TM. 2017. Decoding local field potentials for neural interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 25:1705–14
    [Google Scholar]
54. 54. 

    Milekovic T, Sarma AA, Bacher D, Simeral JD, Saab J et al. 2018. Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. J. Neurophysiol. 120:343–60
    [Google Scholar]
55. 55. 

    Flint RD, Lindberg EW, Jordan LR, Miller LE, Slutzky MW. 2012. Accurate decoding of reaching movements from field potentials in the absence of spikes. J. Neural Eng. 9:046006
    [Google Scholar]
56. 56. 

    Nason SR, Vaskov AK, Willsey MS, Welle EJ, An H et al. 2020. A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces. Nat. Biomed. Eng 4:973–83
    [Google Scholar]
57. 57. 

    Pachitariu M, Stringer C, Dipoppa M, Schröder S, Rossi LF et al. 2017. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. bioRxiv 061507. https://doi.org/10.1101/061507
    [Crossref]
58. 58. 

    Chen TW, Wardill TJ, Sun Y, Pulver SR, Renninger SL et al. 2013. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499:295–300
    [Google Scholar]
59. 59. 

    Clancy KB, Koralek AC, Costa RM, Feldman DE, Carmena JM. 2014. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 17:807–9
    [Google Scholar]
60. 60. 

    Liberti W, Gong XL, Roseberry T, Costa RM, Carmena JM. 2019. Local network coordination supports neuroprosthetic control. 2019 9th International IEEE/EMBS Conference on Neural Engineering558–61 Piscataway, NJ: IEEE
    [Google Scholar]
61. 61. 

    Trautmann EM, O'Shea DJ, Sun X, Marshel JH, Crow A et al. 2019. Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. bioRxiv 780486. https://doi.org/10.1101/780486
    [Crossref]
62. 62. 

    Bollimunta A, Santacruz SR, Eaton RW, Xu PS, Morrison JH et al. 2020. Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque. bioRxiv 2020.04.10.996116. https://doi.org/10.1101/2020.04.10.996116
    [Crossref]
63. 63. 

    Kleinlogel S, Vogl C, Jeschke M, Neef J, Moser T. 2020. Emerging approaches for restoration of hearing and vision. Physiol. Rev. 100:1467–525
    [Google Scholar]
64. 64. 

    Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB. 2008. Cortical control of a prosthetic arm for self-feeding. Nature 453:1098–101
    [Google Scholar]
65. 65. 

    Churchland MM, Cunningham JP, Kaufman MT, Foster JD, Nuyujukian P et al. 2012. Neural population dynamics during reaching. Nature 487:51–56
    [Google Scholar]
66. 66. 

    Gallego JA, Perich MG, Chowdhury RH, Solla SA, Miller LE. 2020. Long-term stability of cortical population dynamics underlying consistent behavior. Nat. Neurosci. 23:260–70
    [Google Scholar]
67. 67. 

    Rubin A, Sheintuch L, Brande-Eilat N, Pinchasof O, Rechavi Y et al. 2019. Revealing neural correlates of behavior without behavioral measurements. Nat. Commun. 10:4745
    [Google Scholar]
68. 68. 

    Bacher D, Jarosiewicz B, Masse NY, Stavisky SD, Simeral JD et al. 2015. Neural point-and-click communication by a person with incomplete locked-in syndrome. Neurorehabil. Neural Repair 29:462–71
    [Google Scholar]
69. 69. 

    Kim SP, Simeral JD, Hochberg LR, Donoghue JP, Black MJ. 2008. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5:455–76
    [Google Scholar]
70. 70. 

    Malik WQ, Truccolo W, Brown EN, Hochberg LR. 2011. Efficient decoding with steady-state Kalman filter in neural interface systems. IEEE Trans. Neural Syst. Rehabil. Eng. 19:25–34
    [Google Scholar]
71. 71. 

    Inoue Y, Mao H, Suway SB, Orellana J, Schwartz AB. 2018. Decoding arm speed during reaching. Nat. Commun. 9:5243
    [Google Scholar]
72. 72. 

    Li S, Li J, Li Z. 2016. An improved unscented Kalman filter based decoder for cortical brain-machine interfaces. Front. Neurosci. 10:587
    [Google Scholar]
73. 73. 

    Brandman DM, Burkhart MC, Kelemen J, Franco B, Harrison MT, Hochberg LR. 2018. Robust closed-loop control of a cursor in a person with tetraplegia using Gaussian process regression. Neural Comput 30:2986–3008
    [Google Scholar]
74. 74. 

    Burkhart MC, Brandman DM, Franco B, Hochberg LR, Harrison MT. 2020. The discriminative Kalman filter for Bayesian filtering with nonlinear and nongaussian observation models. Neural Comput 32:969–1017
    [Google Scholar]
75. 75. 

    Hosman T, Vilela M, Milstein D, Kelemen JN, Brandman DM et al. 2019. BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation. 2019 9th International IEEE/EMBS Conference on Neural Engineering1066–71 Piscataway, NJ: IEEE
    [Google Scholar]
76. 76. 

    Tseng PH, Urpi NA, Lebedev M, Nicolelis M. 2019. Decoding movements from cortical ensemble activity using a long short-term memory recurrent network. Neural Comput 31:1085–113
    [Google Scholar]
77. 77. 

    Sussillo D, Nuyujukian P, Fan JM, Kao JC, Stavisky SD et al. 2012. A recurrent neural network for closed-loop intracortical brain–machine interface decoders. J. Neural Eng. 9:026027
    [Google Scholar]
78. 78. 

    Vargas-Irwin CE, Feldman JM, King B, Simeral JD, Sorice BL et al. 2018. Watch, imagine, attempt: Motor cortex single-unit activity reveals context-dependent movement encoding in humans with tetraplegia. Front. Hum. Neurosci. 12:450
    [Google Scholar]
79. 79. 

    Rastogi A, Vargas-Irwin CE, Willett FR, Abreu J, Crowder DC et al. 2020. Neural representation of observed, imagined, and attempted grasping force in motor cortex of individuals with chronic tetraplegia. Sci. Rep. 10:1429
    [Google Scholar]
80. 80. 

    Hatsopoulos N, Joshi J, O'Leary JG. 2004. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. J. Neurophysiol. 92:1165–74
    [Google Scholar]
81. 81. 

    Willett FR, Young DR, Murphy BA, Memberg WD, Blabe CH et al. 2019. Principled BCI decoder design and parameter selection using a feedback control model. Sci. Rep. 9:8881
    [Google Scholar]
82. 82. 

    Chase SM, Schwartz AB, Kass RE. 2009. Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain–computer interface algorithms. Neural Netw 22:1203–13
    [Google Scholar]
83. 83. 

    Koyama S, Chase SM, Whitford AS, Velliste M, Schwartz AB, Kass RE 2010. Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control. J. Comput. Neurosci. 29:73–87
    [Google Scholar]
84. 84. 

    Taylor DM, Helms Tillery SI, Schwartz AB 2002. Direct cortical control of 3D neuroprosthetic devices. Science 296:1829–32
    [Google Scholar]
85. 85. 

    Gowda S, Orsborn AL, Overduin SA, Moorman HG, Carmena JM. 2014. Designing dynamical properties of brain–machine interfaces to optimize task-specific performance. IEEE Trans. Neural Syst. Rehabil. Eng. 22:911–20
    [Google Scholar]
86. 86. 

    Shanechi MM, Orsborn AL, Moorman HG, Gowda S, Dangi S, Carmena JM. 2017. Rapid control and feedback rates enhance neuroprosthetic control. Nat. Commun. 8:13825
    [Google Scholar]
87. 87. 

    Gilja V, Nuyujukian P, Chestek CA, Cunningham JP, Yu BM et al. 2012. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15:1752–57
    [Google Scholar]
88. 88. 

    Willett FR, Murphy BA, Young D, Memberg WD, Blabe CH et al. 2018. A comparison of intention estimation methods for decoder calibration in intracortical brain–computer interfaces. IEEE Trans. Biomed. Eng. 65:2066–78
    [Google Scholar]
89. 89. 

    Jarosiewicz B, Sarma AA, Bacher D, Masse NY, Simeral JD et al. 2015. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Sci. Transl. Med. 7:313ra179
    [Google Scholar]
90. 90. 

    Willett FR, Pandarinath C, Jarosiewicz B, Murphy BA, Memberg WD et al. 2017. Feedback control policies employed by people using intracortical brain–computer interfaces. J. Neural Eng. 14:016001
    [Google Scholar]
91. 91. 

    Todorov E. 2004. Optimality principles in sensorimotor control. Nat. Neurosci. 7:907–15
    [Google Scholar]
92. 92. 

    Shanechi MM, Orsborn AL, Carmena JM. 2016. Robust brain-machine interface design using optimal feedback control modeling and adaptive point process filtering. PLOS Comput. Biol. 12:e1004730
    [Google Scholar]
93. 93. 

    Benyamini M, Zacksenhouse M. 2015. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces. Front. Syst. Neurosci. 9:71
    [Google Scholar]
94. 94. 

    Lagang M, Srinivasan L. 2013. Stochastic optimal control as a theory of brain-machine interface operation. Neural Comput 25:374–417
    [Google Scholar]
95. 95. 

    Godlove JM, Whaite EO, Batista AP. 2014. Comparing temporal aspects of visual, tactile, and micro-stimulation feedback for motor control. J. Neural Eng. 11:046025
    [Google Scholar]
96. 96. 

    Sombeck JT, Miller LE. 2019. Short reaction times in response to multi-electrode intracortical micro-stimulation may provide a basis for rapid movement-related feedback. J. Neural Eng. 17:016013
    [Google Scholar]
97. 97. 

    Weiss JM, Flesher SN, Franklin R, Collinger JL, Gaunt RA. 2018. Artifact-free recordings in human bidirectional brain–computer interfaces. J. Neural Eng. 16:016002
    [Google Scholar]
98. 98. 

    Ganzer PD, Colachis SC, Schwemmer MA, Friedenberg DA, Dunlap CF et al. 2020. Restoring the sense of touch using a sensorimotor demultiplexing neural interface. Cell 181:763–73.e12
    [Google Scholar]
99. 99. 

    Prsa M, Galiñanes GL, Huber D. 2017. Rapid integration of artificial sensory feedback during operant conditioning of motor cortex neurons. Neuron 93:929–39.e6
    [Google Scholar]
100. 100. 

     Abbasi A, Goueytes D, Shulz DE, Ego-Stengel V, Estebanez L. 2018. A fast intracortical brain–machine interface with patterned optogenetic feedback. J. Neural Eng. 15:046011
     [Google Scholar]
101. 101. 

     Lu Y, Truccolo W, Wagner FB, Vargas-Irwin CE, Ozden I et al. 2015. Optogenetically induced spatiotemporal gamma oscillations and neuronal spiking activity in primate motor cortex. J. Neurophysiol. 113:3574–87
     [Google Scholar]
102. 102. 

     Yazdan-Shahmorad A, Silversmith DB, Kharazia V, Sabes PN. 2018. Targeted cortical reorganization using optogenetics in non-human primates. eLife 7:e31034
     [Google Scholar]
103. 103. 

     Hotson G, Smith RJ, Rouse AG, Schieber MH, Thakor NV, Wester BA. 2016. High precision neural decoding of complex movement trajectories using recursive bayesian estimation with dynamic movement primitives. IEEE Robot. Autom. Lett. 1:676–83
     [Google Scholar]
104. 104. 

     Moritz CT, Fetz EE. 2011. Volitional control of single cortical neurons in a brain-machine interface. J. Neural Eng. 8:025017
     [Google Scholar]
105. 105. 

     Neely RM, Koralek AC, Athalye VR, Costa RM, Carmena JM. 2018. Volitional modulation of primary visual cortex activity requires the basal ganglia. Neuron 97:1356–68.e4
     [Google Scholar]
106. 106. 

     Law AJ, Rivlis G, Schieber MH. 2014. Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons. J. Neurophysiol. 112:1528–48
     [Google Scholar]
107. 107. 

     Zhou X, Tien RN, Ravikumar S, Chase SM. 2019. Distinct types of neural reorganization during long-term learning. J. Neurophysiol. 121:1329–41
     [Google Scholar]
108. 108. 

     You AK, Liu B, Singhal A, Gowda S, Moorman H et al. 2020. Flexible modulation of neural variance facilitates neuroprosthetic skill learning. bioRxiv 817346. https://doi.org/10.1101/817346
     [Crossref]
109. 109. 

     Pierella C, Casadio M, Mussa-Ivaldi FA, Solla SA. 2019. The dynamics of motor learning through the formation of internal models. PLOS Comput. Biol. 15:e1007118
     [Google Scholar]
110. 110. 

     Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DV et al. 2003. Learning to control a brain–machine interface for reaching and grasping by primates. PLOS Biol 1:e42
     [Google Scholar]
111. 111. 

     Wander JD, Blakely T, Miller KJ, Weaver KE, Johnson LA et al. 2013. Distributed cortical adaptation during learning of a brain-computer interface task. PNAS 110:10818–23
     [Google Scholar]
112. 112. 

     Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI et al. 2014. Neural constraints on learning. Nature 512:423–26
     [Google Scholar]
113. 113. 

     Feulner B, Clopath C. 2020. Neural manifold under plasticity in a goal driven learning behaviour. bioRxiv 2020.02.21.959163. https://doi.org/10.1101/2020.02.21.959163
     [Crossref]
114. 114. 

     Sakellaridi S, Christopoulos VN, Aflalo T, Pejsa KW, Rosario ER et al. 2019. Intrinsic variable learning for brain-machine interface control by human anterior intraparietal cortex. Neuron 102:694–705.e3
     [Google Scholar]
115. 115. 

     Perich MG, Miller LE. 2017. Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning. Exp. Brain Res. 235:2689–704
     [Google Scholar]
116. 116. 

     Perich MG, Gallego JA, Miller LE. 2018. A neural population mechanism for rapid learning. Neuron 100:964–976.e7
     [Google Scholar]
117. 117. 

     Golub MD, Sadtler PT, Oby ER, Quick KM, Ryu SI et al. 2018. Learning by neural reassociation. Nat. Neurosci. 21:607–16
     [Google Scholar]
118. 118. 

     Oby ER, Golub MD, Hennig JA, Degenhart AD, Tyler-Kabara EC et al. 2019. New neural activity patterns emerge with long-term learning. PNAS 116:15210–15
     [Google Scholar]
119. 119. 

     Ganguly K, Carmena JM. 2009. Emergence of a stable cortical map for neuroprosthetic control. PLOS Biol 7:e1000153
     [Google Scholar]
120. 120. 

     You A, Zippi EL, Carmena JM. 2019. Large-scale neural consolidation in BMI learning. 2019 9th International IEEE/EMBS Conference on Neural Engineering603–6 Piscataway, NJ: IEEE
     [Google Scholar]
121. 121. 

     Jarosiewicz B, Chase SM, Fraser GW, Velliste M, Kass RE, Schwartz AB 2008. Functional network reorganization during learning in a brain-computer interface paradigm. PNAS 105:19486–91
     [Google Scholar]
122. 122. 

     Zhang Y, Chase SM. 2018. Optimizing the usability of brain-computer interfaces. Neural Comput 30:1323–58
     [Google Scholar]
123. 123. 

     Oweiss KG, Badreldin IS. 2015. Neuroplasticity subserving the operation of brain–machine interfaces. Neurobiol. Dis. 83:161–71
     [Google Scholar]
124. 124. 

     Driscoll LN, Pettit NL, Minderer M, Chettih SN, Harvey CD. 2017. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170:986–999.e16
     [Google Scholar]
125. 125. 

     Rubin A, Geva N, Sheintuch L, Ziv Y. 2015. Hippocampal ensemble dynamics timestamp events in long-term memory. eLife 4:e12247
     [Google Scholar]
126. 126. 

     Dhawale AK, Poddar R, Wolff SB, Normand VA, Kopelowitz E, Ölveczky BP. 2017. Automated long-term recording and analysis of neural activity in behaving animals. eLife 6:e27702
     [Google Scholar]
127. 127. 

     Rule ME, O'Leary T, Harvey CD. 2019. Causes and consequences of representational drift. Curr. Opin. Neurobiol. 58:141–47
     [Google Scholar]
128. 128. 

     You A, Singhal A, Moorman H, Gowda S, Carmena JM. 2019. Neural correlates of control of a kinematically redundant brain-machine interface. 2019 9th International IEEE/EMBS Conference on Neural Engineering554–57 Piscataway, NJ: IEEE
     [Google Scholar]
129. 129. 

     Ganguly K, Dimitrov DF, Wallis JD, Carmena JM. 2011. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14:662–67
     [Google Scholar]
130. 130. 

     Lansdell B, Milovanovic I, Mellema C, Fetz EE, Fairhall AL, Moritz CT. 2020. Reconfiguring motor circuits for a joint manual and BCI task. IEEE Trans. Neural Syst. Rehabil. Eng. 28:248–57
     [Google Scholar]
131. 131. 

     Lalazar H, Murray J, Abbott L, Vaadia E. 2019. Population subspaces reflect movement intention for arm and brain-machine interface control. bioRxiv 688259. https://doi.org/10.1101/688259
     [Crossref]
132. 132. 

     Carmena JM, Lebedev MA, Henriquez CS, Nicolelis MAL. 2005. Stable ensemble performance with single-neuron variability during reaching movements in primates. J. Neurosci. 25:10712–16
     [Google Scholar]
133. 133. 

     Sussillo D, Stavisky SD, Kao JC, Ryu SI, Shenoy KV. 2016. Making brain–machine interfaces robust to future neural variability. Nat. Commun. 7:13749
     [Google Scholar]
134. 134. 

     Rule ME, Loback AR, Raman DV, Driscoll LN, Harvey CD, O'Leary T 2020. Stable task information from an unstable neural population. eLife 9:e51121
     [Google Scholar]
135. 135. 

     Orsborn A, Moorman H, Overduin S, Shanechi M, Dimitrov D, Carmena J. 2014. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82:1380–93
     [Google Scholar]
136. 136. 

     Dangi S, Gowda S, Moorman HG, Orsborn AL, So K et al. 2014. Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Comput 26:1811–39
     [Google Scholar]
137. 137. 

     Merel J, Pianto DM, Cunningham JP, Paninski L. 2015. Encoder-decoder optimization for brain-computer interfaces. PLOS Comput. Biol. 11:e1004288
     [Google Scholar]
138. 138. 

     Pohlmeyer EA, Mahmoudi B, Geng S, Prins NW, Sanchez JC. 2014. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization. PLOS ONE 9:e87253
     [Google Scholar]
139. 139. 

     Zhang P, Ma X, Chen L, Zhou J, Wang C et al. 2018. Decoder calibration with ultra small current sample set for intracortical brain–machine interface. J. Neural Eng. 15:026019
     [Google Scholar]
140. 140. 

     Brandman DM, Hosman T, Saab J, Burkhart MC, Shanahan BE et al. 2018. Rapid calibration of an intracortical brain computer interface for people with tetraplegia. J. Neural Eng. 15:026007
     [Google Scholar]
141. 141. 

     Xing D, Aghagolzadeh M, Truccolo W, Borton D. 2019. Low-dimensional motor cortex dynamics preserve kinematics information during unconstrained locomotion in nonhuman primates. Front. Neurosci. 13:1046
     [Google Scholar]
142. 142. 

     Aghagolzadeh M, Truccolo W. 2016. Inference and decoding of motor cortex low-dimensional dynamics via latent state-space models. IEEE Trans. Neural Syst. Rehabil. Eng. 24:272–82
     [Google Scholar]
143. 143. 

     Dyer EL, Gheshlaghi Azar M, Perich MG, Fernandes HL, Naufel S et al. 2017. A cryptography-based approach for movement decoding. Nat. Biomed. Eng. 1:967–76
     [Google Scholar]
144. 144. 

     Kao JC, Ryu SI, Shenoy KV. 2017. Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces. Sci. Rep. 7:7395
     [Google Scholar]
145. 145. 

     Degenhart AD, Bishop WE, Oby ER, Tyler-Kabara EC, Chase SM et al. 2020. Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity. Nat. Biomed. Eng. 4:672–85
     [Google Scholar]
146. 146. 

     Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT et al. 2019. Accurate estimation of neural population dynamics without spike sorting. Neuron 103:292–308.e4
     [Google Scholar]
147. 147. 

     Milstein DJ, Pacheco JL, Hochberg LR, Simeral JD, Jarosiewicz B, Sudderth EB 2017. Multiscale semi-Markov dynamics for intracortical brain-computer interfaces. Advances in Neural Information Processing Systems 30 I Guyon, UV Luxburg, S Bengio, H Wallach, R Fergus, et al 868–78 Red Hook, NY: Curran
     [Google Scholar]
148. 148. 

     Li H, Hao Y, Zhang S, Wang Y, Chen W, Zheng X. 2017. Prior knowledge of target direction and intended movement selection improves indirect reaching movement decoding. Behav. Neurol. 2017:2182843
     [Google Scholar]
149. 149. 

     Shanechi MM, Williams ZM, Wornell GW, Hu RC, Powers M, Brown EN. 2013. A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design. PLOS ONE 8:e59049
     [Google Scholar]
150. 150. 

     Benyamini M, Nason SR, Chestek CA, Zacksenhouse M. 2019. Neural correlates of error processing during grasping with invasive brain-machine interfaces. 2019 9th International IEEE/EMBS Conference on Neural Engineering215–18 Piscataway, NJ: IEEE
     [Google Scholar]
151. 151. 

     Even-Chen N, Stavisky SD, Kao JC, Ryu SI, Shenoy KV. 2017. Augmenting intracortical brain-machine interface with neurally driven error detectors. J. Neural Eng. 14:066007
     [Google Scholar]
152. 152. 

     Schroeder KE, Irwin ZT, Bullard AJ, Thompson DE, Bentley JN et al. 2017. Robust tactile sensory responses in finger area of primate motor cortex relevant to prosthetic control. J. Neural Eng. 14:046016
     [Google Scholar]
153. 153. 

     Stavisky SD, Kao JC, Ryu SI, Shenoy KV. 2017. Motor cortical visuomotor feedback activity is initially isolated from downstream targets in output-null neural state space dimensions. Neuron 95:195–208.e9
     [Google Scholar]
154. 154. 

     Ramakrishnan A, Byun YW, Rand K, Pedersen CE, Lebedev MA, Nicolelis MA 2017. Cortical neurons multiplex reward-related signals along with sensory and motor information. PNAS 114:E4841–50
     [Google Scholar]
155. 155. 

     Zhao Y, Hessburg JP, Asok Kumar JN, Francis JT 2018. Paradigm shift in sensorimotor control research and brain machine interface control: the influence of context on sensorimotor representations. Front. Neurosci. 12:579
     [Google Scholar]
156. 156. 

     Downey JE, Brane L, Gaunt RA, Tyler-Kabara EC, Boninger ML, Collinger JL. 2017. Motor cortical activity changes during neuroprosthetic-controlled object interaction. Sci. Rep. 7:16947
     [Google Scholar]
157. 157. 

     Schroeder KE, Perkins SM, Wang Q, Churchland MM. 2020. Neural control of virtual ego-motion enabled by an opportunistic decoding strategy. bioRxiv 2019.12.13.862532. https://doi.org/10.1101/2019.12.13.862532
     [Crossref]
158. 158. 

     Sheintuch L, Geva N, Baumer H, Rechavi Y, Rubin A, Ziv Y. 2020. Multiple maps of the same spatial context can stably coexist in the mouse hippocampus. Curr. Biol. 30:1467–76.e6
     [Google Scholar]
159. 159. 

     Gallego JA, Perich MG, Naufel SN, Ethier C, Solla SA, Miller LE. 2018. Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nat. Commun. 9:4233
     [Google Scholar]
160. 160. 

     Sumsky SL, Schieber MH, Thakor NV, Sarma SV, Santaniello S. 2017. Decoding kinematics using task-independent movement-phase-specific encoding models. IEEE Trans. Neural Syst. Rehabil. Eng. 25:2122–32
     [Google Scholar]
161. 161. 

     Williams JJ, Tien RN, Inoue Y, Schwartz AB. 2016. Idle state classification using spiking activity and local field potentials in a brain computer interface. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society1572–75 Piscataway, NJ: IEEE
     [Google Scholar]
162. 162. 

     Sachs NA, Ruiz-Torres R, Perreault EJ, Miller LE. 2016. Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface. J. Neural Eng. 13:016009
     [Google Scholar]
163. 163. 

     Borton DA, Yin M, Aceros J, Nurmikko A. 2013. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J. Neural Eng. 10:026010
     [Google Scholar]
164. 164. 

     Weiss JM, Gaunt RA, Franklin R, Boninger ML, Collinger JL. 2019. Demonstration of a portable intracortical brain-computer interface. Brain-Comput. Interfaces 6:106–17
     [Google Scholar]
165. 165. 

     Gilron R, Little S, Perrone R, Wilt R, de Hemptinne C et al. 2020. Chronic wireless streaming of invasive neural recordings at home for circuit discovery and adaptive stimulation. bioRxiv 2020.02.13.948349. https://doi.org/10.1101/2020.02.13.948349
     [Crossref]
166. 166. 

     Jorge A, Royston DA, Tyler-Kabara EC, Boninger ML, Collinger JL. 2020. Classification of individual finger movements using intracortical recordings in human motor cortex. Neurosurgery 87:630–38
     [Google Scholar]
167. 167. 

     Wilson GH, Stavisky SD, Willett FR, Avansino DT, Kelemen JN et al. 2020. Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus. bioRxiv 2020.06.30.180935. https://doi.org/10.1101/2020.06.30.180935
     [Crossref]
168. 168. 

     Zhang Z, Russell LE, Packer AM, Gauld OM, Häusser M. 2018. Closed-loop all-optical interrogation of neural circuits in vivo. Nat. Methods 15:1037–40
     [Google Scholar]
169. 169. 

     Mitani A, Dong M, Komiyama T. 2018. Brain-computer interface with inhibitory neurons reveals subtype-specific strategies. Curr. Biol. 28:77–83
     [Google Scholar]