Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Cortical Motor Prosthesis

  • Karthikeyan BalasubramanianEmail author
  • Nicholas G. Hatsopoulos
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_705-1


Neuromotor prostheses or, more commonly referred to as brain-machine interfaces (BMIs) or brain-computer interfaces (BCIs), refer to systems controlling prosthetic devices via an interface with ensembles of the neurons often from the cortex. Electrical potentials emanating from neurons in the vicinity of an electrode interface are decoded to extract useful control signals for external devices, typically an artificial limb or a robot. Nonelectric potentials such as the metabolic signals are also being used in some BMIs.


Cortically controlled BMIs utilize voluntary modulations of cortical neurons in controlling an external prosthetic device. The system-level architecture of a BMI setup is shown in Fig. 1. Individual neural signals, i.e., action potential spikes, local field potentials, ECoGs, and EEGs, or a combination of these signals, can be used to control a motor prosthesis. Temporal and spectral modulations of these signals are typically mapped (or decoded)...


Kalman Filter Local Field Potential Neural Signal Voluntary Modulation Grasp Aperture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.


  1. Andersen RA, Musallam S, Pesaran B (2004) Selecting the signals for a brain-machine interface. Curr Opin Neurobiol 14(6):720–726PubMedCrossRefGoogle Scholar
  2. Aniruddha C, Vikram A, Ander R, Soumyadipta A, Nitish T (2007) A brain-computer interface with vibrotactile biofeedback for haptic information. J NeuroEng Rehab 4Google Scholar
  3. Ashe J, Georgopoulos AP (1994) Movement parameters and neural activity in motor cortex and area 5. Cereb Cortex 4(6):590–600PubMedCrossRefGoogle Scholar
  4. Badreldin I, Southerland J, Mukta V, Eleryan A, Balasubramanian K, Fagg A, Hatsopoulos N, Oweiss K (2013) Unsupervised decoder initialization for brain-machine interfaces using neural state space dynamics. In: Neural engineering (NER), 2013 international IEEE/EMBS conference on, San Diego, California, USA, pp 997–1000Google Scholar
  5. Balasubramanian K, Southerland J, Mukta V, Qian K, Eleryan A, Fagg AH, Sluzky M, Oweiss K, Hatsopoulos N (2013) Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation. In: Engineering in medicine and biology society (EMBC), 2013 annual international conference of the IEEE, Osaka, Japan, pp 303–306Google Scholar
  6. Berg J, Dammann J, Tenore F, Tabot G, Boback J, Manfredi L, Peterson M, Katyal K, Johannes M, Makhlin A, Wilcox R, Franklin R, Vogelstein R, Hatsopoulos N, Bensmaia S (2013) Behavioral demonstration of a somatosensory neuroprosthesis. Neural Syst Rehab Eng IEEE Trans 21(3):500–507CrossRefGoogle Scholar
  7. Black MJ, Bienenstock E, Donoghue JP, Serruya M, Wu W, Gao Y (2003) Connecting brains with machines: the neural control of 2D cursor movement. In: Neural engineering (NER), 2003 international IEEE/EMBS conference on, Capri Island, Italy, pp 580–583Google Scholar
  8. Black MJ, Donoghue JP (2007) Probabilistically modeling and decoding neural population activity in motor cortex. In: Guido Dornhege THDJM, del Millán JR, Müller K-R (eds) Toward Brain-Computer Interfacing, MIT Press, Cambridge, MA, pp. 147–159Google Scholar
  9. Brown EN, Nguyen DP, Frank LM, Wilson MA, Solo V (2001) An analysis of neural receptive field plasticity by point process adaptive filtering. Proc Natl Acad Sci 98(21):12261–12266PubMedCentralPubMedCrossRefGoogle Scholar
  10. Brown EN, Barbieri R, Eden UT, Frank LM (2003) Likelihood methods for neural spike train data analysis. In: Feng J (ed.), Computational neuroscience: A comprehensive approach. Chapman & Hall/CRC London, PP 253–286Google Scholar
  11. Brunner P, Ritaccio AL, Lynch TM, Emrich JF, Wilson JA, Williams JC, Aarnoutse EJ, Ramsey NF, Leuthardt EC, Bischof H, Schalk G (2009) A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans. Epilepsy Behav 15(3):278–286PubMedCentralPubMedCrossRefGoogle Scholar
  12. Cabel DW, Cisek P, Scott SH (2001) Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. J Neurophysiol 86(4):2102–2108PubMedGoogle Scholar
  13. Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA (2003) Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol 1(2):e42PubMedCentralPubMedCrossRefGoogle Scholar
  14. Chebat D-R, Schneider FC, Kupers R, Ptito M (2011) Navigation with a sensory substitution device in congenitally blind individuals. Neuroreport 22(7):342–347PubMedCrossRefGoogle Scholar
  15. Chen Z (2003) Bayesian filtering: from kalman filters to particle filters, and beyond. Statistics 182(1):1–69CrossRefGoogle Scholar
  16. Cheney PD, Fetz EE (1980) Functional classes of primate corticomotoneuronal cells and their relation to active force. J Neurophysiol 44(4):773–791PubMedGoogle Scholar
  17. Cincotti F, Kauhanen L, Aloise F, Palomäki T, Caporusso N, Jylänki P, Mattia D, Babiloni F, Vanacker G, Nuttin M, Marciani MG, del Millán JR (2007) Vibrotactile feedback for brain-computer interface operation. Intell Neurosci 12Google Scholar
  18. Ciocarlie M, Goldfeder C, Allen P (2007) Dexterous grasping via eigengrasps: a low-dimensional approach to a high-complexity problem. In: Proceedings of the robotics: science and systems 2007 manipulation workshop - sensing and adapting to the real world. Robotics: science and systems conference, Atlanta, Georgia USAGoogle Scholar
  19. Clanton ST, McMorland AJ, Zohny Z, Jeffries SM, Rasmussen RG, Flesher SN, Velliste M (2013) Seven degree of freedom cortical control of a robotic arm. In: Brain-computer interface research. Springer, Berlin, pp 73–81CrossRefGoogle Scholar
  20. Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, McMorland AJ, Velliste M, Boninger ML, Schwartz AB (2013) High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet 381(9866):557–564CrossRefGoogle Scholar
  21. Corke P (2011) Robotics, vision and control: fundamental algorithms in MATLAB, vol 73. Springer, BerlinCrossRefGoogle Scholar
  22. Crago PE, Houk JC, Hasan Z (1976) Regulatory actions of human stretch reflex. J Neurophysiol 39(5):925–935PubMedGoogle Scholar
  23. Donoghue JP, Sanes JN, Hatsopoulos NG, Gaál G (1998) Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements. J Neurophysiol 79(1):159–173PubMedGoogle Scholar
  24. Dushanova J, Donoghue J (2010) Neurons in primary motor cortex engaged during action observation. Eur J Neurosci 31(2):386–398PubMedCentralPubMedCrossRefGoogle Scholar
  25. Eide PK, Jørum E, Stenehjem AE (1996) Somatosensory findings in patients with spinal cord injury and central dysaesthesia pain. J Neurol Neurosurg Psychiatry 60(4):411–415PubMedCentralPubMedCrossRefGoogle Scholar
  26. Fagg A, Ojakangas G, Miller L, Hatsopoulos N (2009) Kinetic trajectory decoding using motor cortical ensembles. Neural Syst Rehab Eng IEEE Trans 17(5):487–496CrossRefGoogle Scholar
  27. Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523PubMedCrossRefGoogle Scholar
  28. Fetz EE (1969) Operant conditioning of cortical unit activity. Science 163(3870):955–958PubMedCrossRefGoogle Scholar
  29. Fetz EE, Finocchio DV (1971) Operant conditioning of specific patterns of neural and muscular activity. Science 174(4007):431–435PubMedCrossRefGoogle Scholar
  30. Fetz E, Finocchio D (1975) Correlations between activity of motor cortex cells and arm muscles during operantly conditioned response patterns. Exp Brain Res 23(3):217–240PubMedGoogle Scholar
  31. Finnerup NB, Gyldensted C, Fuglsang-Frederiksen A, Bach FW, Jensen TS (2004) Sensory perception in complete spinal cord injury. Acta Neurol Scand 109(3):194–199PubMedCrossRefGoogle Scholar
  32. Flash T, Hogan N (1985) The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 5(7):1688–1703PubMedGoogle Scholar
  33. Flint R, Wright Z, Slutzky M (2012) Control of a biomimetic brain machine interface with local field potentials: Performance and stability of a static decoder over 200 days. In: Engineering in medicine and biology society (EMBC), 2012 annual international conference of the IEEE, San Diego, California, USA, pp 6719–6722Google Scholar
  34. Flint RD, Wright ZA, Scheid MR, Slutzky MW (2013) Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J Neural Eng 10(5):056005PubMedCrossRefGoogle Scholar
  35. Fu Q, Suarez J, Ebner T (1993) Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys. J Neurophysiol 70(5):2097–2116PubMedGoogle Scholar
  36. Fu Q, Flament D, Coltz J, Ebner T (1995) Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons. J Neurophysiol 73(2):836–854PubMedGoogle Scholar
  37. Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233(4771):1416–1419PubMedCrossRefGoogle Scholar
  38. Gilja V, Nuyujukian P, Chestek CA, Cunningham JP, Byron MY, Fan JM, Church-land MM, Kaufman MT, Kao JC, Ryu SI, Shenoy KV (2012) A high-performance neural prosthesis enabled by control algorithm design. Nature Neurosci 15:1752–1757PubMedCentralPubMedCrossRefGoogle Scholar
  39. Grill WM, Norman SE, Bellamkonda RV (2009) Implanted neural interfaces: biochallenges and engineered solutions. Annu Rev Biomed Eng 11:1–24PubMedCrossRefGoogle Scholar
  40. Hatsopoulos NG, Donoghue JP (2009) The science of neural interface systems. Ann Rev Neurosci 32:249PubMedCentralPubMedCrossRefGoogle Scholar
  41. Hatsopoulos N, Joshi J, O’Leary JG (2004) Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. J Neurophysiol 92(2):1165–1174PubMedCrossRefGoogle Scholar
  42. Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099):164–171PubMedCrossRefGoogle Scholar
  43. Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, van der Smagt P et al (2012) Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485(7398):372–375PubMedCentralPubMedCrossRefGoogle Scholar
  44. Hwang EJ, Andersen RA (2013) The utility of multichannel local field potentials for brain-machine interfaces. J Neural Eng 10(4):046005PubMedCrossRefGoogle Scholar
  45. Ingram JN, Körding KP, Howard IS, Wolpert DM (2008) The statistics of natural hand movements. Exp Brain Res 188(2):223–236PubMedCentralPubMedCrossRefGoogle Scholar
  46. Kaczmarek K, Webster J, Bach-y Rita P, Tompkins WJ (1991) Electrotactile and vibrotactile displays for sensory substitution systems. Biom Eng IEEE Trans 38(1):1–16CrossRefGoogle Scholar
  47. Katzner S, Nauhaus I, Benucci A, Bonin V, Ringach DL, Carandini M (2009) Local origin of field potentials in visual cortex. Neuron 61(1):35–41PubMedCentralPubMedCrossRefGoogle Scholar
  48. Kennedy PR, Bakay RA (1998) Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9(8):1707–1711PubMedCrossRefGoogle Scholar
  49. Kennedy PR, Bakay RA, Moore MM, Adams K, Goldwaithe J (2000) Direct control of a computer from the human central nervous system. Rehab Eng IEEE Trans 8(2):198–202CrossRefGoogle Scholar
  50. Kennedy P, Andreasen D, Ehirim P, King B, Kirby T, Mao H, Moore M (2004) Using human extra-cortical local field potentials to control a switch. J Neural Eng 1(2):72PubMedCrossRefGoogle Scholar
  51. Lawhern V, Wu W, Hatsopoulos N, Paninski L (2010) Population decoding of motor cortical activity using a generalized linear model with hidden states. J Neurosci Methods 189(2):267–280PubMedCentralPubMedCrossRefGoogle Scholar
  52. Lebedev MA, Nicolelis MA (2011) Toward a whole-body neuroprosthetic, chapter-3. In: Jens Schouenborg MG, Danielsen N (eds) Brain machine interfaces: implications for science, clinical practice and society, vol 194, Progress in brain research. Elsevier, Amsterdam, pp 47–60CrossRefGoogle Scholar
  53. Lebedev MA, Carmena JM, O’Doherty JE, Zacksenhouse M, Henriquez CS, Principe JC, Nicolelis MA (2005) Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J Neurosci 25(19):4681–4693PubMedCrossRefGoogle Scholar
  54. Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An EEG-based BCI system for 2-d cursor control by combining mu/beta rhythm and p300 potential. Biomed Eng IEEE Trans 57(10):2495–2505CrossRefGoogle Scholar
  55. Mason C, Gomez J, Ebner T (2001) Hand synergies during reach-to-grasp. J Neurophysiol 86(6):2896–2910PubMedGoogle Scholar
  56. McFarland DJ, Lefkowicz AT, Wolpaw JR (1997) Design and operation of an EEG-based brain-computer interface with digital signal processing technology. Behav Res Methods Instrum Comput 29(3):337–345CrossRefGoogle Scholar
  57. McFarland DJ, Sarnacki WA, Wolpaw JR (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7(3):036007PubMedCentralPubMedCrossRefGoogle Scholar
  58. Milekovic T, Fischer J, Pistohl T, Ruescher J, Schulze-Bonhage A, Aertsen A, Rickert J, Ball T, Mehring C (2012) An online brain–machine interface using decoding of movement direction from the human electrocorticogram. J Neural Eng 9(4):046003PubMedCrossRefGoogle Scholar
  59. Mountcastle VB, LaMotte RH, Carli G (1972) Detection thresholds for stimuli in humans and monkeys: comparison with threshold events in mechanoreceptive afferent nerve fibers innervating the monkey hand. J Neurophysiol 35:122–136PubMedGoogle Scholar
  60. Mountney J, Obeid I, Silage D (2011). Modular particle filtering FPGA hardware architecture for brain machine interfaces. In: Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE, pp 4617–4620Google Scholar
  61. Mountney J, Sobel M, Obeid I (2009). Bayesian auxiliary particle filters for estimating neural tuning parameters. In: Engineering in medicine and biology society. EMBC 2009. Annual international conference of the IEEE, Minneapolis, Minnesota, USA, pp 5705–5708Google Scholar
  62. Nemec B, Zlajpah L (2000) Null space velocity control with dynamically consistent pseudo-inverse. Robotica 18(5):513–518CrossRefGoogle Scholar
  63. Nicolelis MA (2003) Brain-machine interfaces to restore motor function and probe neural circuits. Nat Rev Neurosci 4(5):417–422PubMedCrossRefGoogle Scholar
  64. Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kübler A (2008a) An auditory brain-computer interface (bci). J Neurosci Methods 167(1):43–50PubMedCrossRefGoogle Scholar
  65. Nijboer F, Sellers E, Mellinger J, Jordan M, Matuz T, Furdea A, Halder S, Mochty U, Krusienski D, Vaughan T, Wolpaw J, Birbaumer N, Kübler A (2008b) A p300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119(8):1909–1916PubMedCentralPubMedCrossRefGoogle Scholar
  66. O’Doherty JE, Lebedev M, Hanson TL, Fitzsimmons N, Nicolelis MA (2009) A brain-machine interface instructed by direct intracortical microstimulation. Front Integ Neurosci 3(20)Google Scholar
  67. O’Doherty JE, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, Nicolelis MA (2011) Active tactile exploration using a brain-machine-brain interface. Nature 479(7372):228–231PubMedCentralPubMedCrossRefGoogle Scholar
  68. Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP (2004) Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J Neurophysiol 91(1):515–532PubMedCrossRefGoogle Scholar
  69. Patterson PE, Katz JA (1992) Design and evaluation of a sensory feedback system that provides grasping pressure in a myoelectric hand. J Rehabil Res Dev 29(1):1–8PubMedCrossRefGoogle Scholar
  70. Santello M (2002) Kinematic synergies for the control of hand shape. Arch Italiennes de Biologie 140(3):221–228Google Scholar
  71. Santhanam G, Ryu SI, Byron MY, Afshar A, Shenoy KV (2006) A high-performance brain–computer interface. Nature 442(7099):195–198PubMedCrossRefGoogle Scholar
  72. Schalk G, Kubánek J, Miller K, Anderson N, Leuthardt E, Ojemann J, Limbrick D, Moran D, Gerhardt L, Wolpaw J (2007) Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng 4(3):264PubMedCrossRefGoogle Scholar
  73. Schreuder M, Blankertz B, Tangermann M (2010) A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS ONE 5(4):e9813PubMedCentralPubMedCrossRefGoogle Scholar
  74. Seel N (2012) Operant conditioning. In: Seel N (ed) Encyclopedia of the sciences of learning. Springer, New York, p 2526CrossRefGoogle Scholar
  75. Sergio LE, Hamel-Pâquet C, Kalaska JF (2005) Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks. J Neurophysiol 94(4):2353–2378PubMedCrossRefGoogle Scholar
  76. Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP (2002) Brain-machine interface: instant neural control of a movement signal. Nature 416(6877):141–142PubMedCrossRefGoogle Scholar
  77. Shadmehr R (2005) The computational neurobiology of reaching and pointing: a foundation for motor learning. MIT Press, Cambridge, MAGoogle Scholar
  78. Shpigelman L, Lalazar H, Vaadia E (2008) Kernel-arma for hand tracking and brainmachine interfacing during 3d motor control. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems. Curran Associates, Inc. Red Hook, NY, 21:1489–1496Google Scholar
  79. Sitaram R, Caria A, Birbaumer N (2009) Hemodynamic brain-computer interfaces for communication and rehabilitation. Neural Netw 22(9):1320–1328PubMedCrossRefGoogle Scholar
  80. Slutzky MW, Jordan LR, Krieg T, Chen M, Mogul DJ, Miller LE (2010) Optimal spacing of surface electrode arrays for brain–machine interface applications. J Neural Eng 7(2):026004CrossRefGoogle Scholar
  81. Smith A, Hepp-Reymond MC, Wyss U (1975) Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles. Exp Brain Res 23(3):315–332PubMedGoogle Scholar
  82. Suminski AJ, Tkach DC, Hatsopoulos NG (2009) Exploiting multiple sensory modalities in brain-machine interfaces. Neural Netw 22(9):1224–1234PubMedCentralPubMedCrossRefGoogle Scholar
  83. Suminski AJ, Tkach DC, Fagg AH, Hatsopoulos NG (2010) Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J Neurosci 30(50):16777–16787PubMedCentralPubMedCrossRefGoogle Scholar
  84. Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J (1996) On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force. Exp Brain Res 109(3):367–376PubMedGoogle Scholar
  85. Taylor DM, Tillery SIH, Schwartz AB (2002) Direct cortical control of 3d neuro-prosthetic devices. Science 296(5574):1829–1832PubMedCrossRefGoogle Scholar
  86. Tkach D, Reimer J, Hatsopoulos NG (2008) Observation-based learning for brain–machine interfaces. Curr Opin Neurobiol 18(6):589–594PubMedCentralPubMedCrossRefGoogle Scholar
  87. Wander JD, Blakely T, Miller KJ, Weaver KE, Johnson LA, Olson JD, Fetz EE, Rao RPN, Ojemann JG (2013) Distributed cortical adaptation during learning of a brain-computer interface task. Proc Natl Acad Sci 110(26):10818–10823PubMedCentralPubMedCrossRefGoogle Scholar
  88. Wang W, Chan SS, Heldman DA, Moran DW (2007) Motor cortical representation of position and velocity during reaching. J Neurophysiol 97(6):4258–4270PubMedCrossRefGoogle Scholar
  89. Wang W, Collinger JL, Degenhart AD, Tyler-Kabara EC, Schwartz AB, Moran DW, Weber DJ, Wodlinger B, Vinjamuri RK, Ashmore RC et al (2013) An electrocorticographic brain interface in an individual with tetraplegia. PLoS One 8(2):e55344PubMedCentralPubMedCrossRefGoogle Scholar
  90. Wolpaw J, Wolpaw EW (2012) Brain-computer interfaces: principles and practice. Oxford University Press, New YorkCrossRefGoogle Scholar
  91. Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ (2006) Bayesian population decoding of motor cortical activity using a kalman filter. Neural Comput 18(1):80–118PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Karthikeyan Balasubramanian
    • 1
    Email author
  • Nicholas G. Hatsopoulos
    • 1
  1. 1.Department of Organismal Biology and AnatomyUniversity of ChicagoChicagoUSA