Definition
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.
Introduction
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)...
Abbreviations
- ALS:
-
Amyotrophic lateral sclerosis
- BCI:
-
Brain-computer interface
- BMI:
-
Brain-machine interface
- ECG:
-
Electrocardiogram
- ECoG:
-
Electrocorticogram
- EEG:
-
Electroencephalogram
- EMG:
-
Electromyogram
- EOG:
-
Electrooculogram
- ERP:
-
Event-related potential
- SCI:
-
Spinal cord injury
References
Andersen RA, Musallam S, Pesaran B (2004) Selecting the signals for a brain-machine interface. Curr Opin Neurobiol 14(6):720–726
Aniruddha C, Vikram A, Ander R, Soumyadipta A, Nitish T (2007) A brain-computer interface with vibrotactile biofeedback for haptic information. J NeuroEng Rehab 4
Ashe J, Georgopoulos AP (1994) Movement parameters and neural activity in motor cortex and area 5. Cereb Cortex 4(6):590–600
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 997–1000
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 303–306
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):500–507
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 580–583
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. 147–159
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):12261–12266
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 253–286
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):278–286
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):2102–2108
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):e42
Chebat D-R, Schneider FC, Kupers R, Ptito M (2011) Navigation with a sensory substitution device in congenitally blind individuals. Neuroreport 22(7):342–347
Chen Z (2003) Bayesian filtering: from kalman filters to particle filters, and beyond. Statistics 182(1):1–69
Cheney PD, Fetz EE (1980) Functional classes of primate corticomotoneuronal cells and their relation to active force. J Neurophysiol 44(4):773–791
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 12
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 USA
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 73–81
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):557–564
Corke P (2011) Robotics, vision and control: fundamental algorithms in MATLAB, vol 73. Springer, Berlin
Crago PE, Houk JC, Hasan Z (1976) Regulatory actions of human stretch reflex. J Neurophysiol 39(5):925–935
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):159–173
Dushanova J, Donoghue J (2010) Neurons in primary motor cortex engaged during action observation. Eur J Neurosci 31(2):386–398
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):411–415
Fagg A, Ojakangas G, Miller L, Hatsopoulos N (2009) Kinetic trajectory decoding using motor cortical ensembles. Neural Syst Rehab Eng IEEE Trans 17(5):487–496
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):510–523
Fetz EE (1969) Operant conditioning of cortical unit activity. Science 163(3870):955–958
Fetz EE, Finocchio DV (1971) Operant conditioning of specific patterns of neural and muscular activity. Science 174(4007):431–435
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):217–240
Finnerup NB, Gyldensted C, Fuglsang-Frederiksen A, Bach FW, Jensen TS (2004) Sensory perception in complete spinal cord injury. Acta Neurol Scand 109(3):194–199
Flash T, Hogan N (1985) The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 5(7):1688–1703
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 6719–6722
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):056005
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):2097–2116
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):836–854
Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233(4771):1416–1419
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 15:1752–1757
Grill WM, Norman SE, Bellamkonda RV (2009) Implanted neural interfaces: biochallenges and engineered solutions. Annu Rev Biomed Eng 11:1–24
Hatsopoulos NG, Donoghue JP (2009) The science of neural interface systems. Ann Rev Neurosci 32:249
Hatsopoulos N, Joshi J, O’Leary JG (2004) Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. J Neurophysiol 92(2):1165–1174
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):164–171
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):372–375
Hwang EJ, Andersen RA (2013) The utility of multichannel local field potentials for brain-machine interfaces. J Neural Eng 10(4):046005
Ingram JN, Körding KP, Howard IS, Wolpert DM (2008) The statistics of natural hand movements. Exp Brain Res 188(2):223–236
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):1–16
Katzner S, Nauhaus I, Benucci A, Bonin V, Ringach DL, Carandini M (2009) Local origin of field potentials in visual cortex. Neuron 61(1):35–41
Kennedy PR, Bakay RA (1998) Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9(8):1707–1711
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):198–202
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):72
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):267–280
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 47–60
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):4681–4693
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):2495–2505
Mason C, Gomez J, Ebner T (2001) Hand synergies during reach-to-grasp. J Neurophysiol 86(6):2896–2910
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):337–345
McFarland DJ, Sarnacki WA, Wolpaw JR (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7(3):036007
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):046003
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 35:122–136
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 4617–4620
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 5705–5708
Nemec B, Zlajpah L (2000) Null space velocity control with dynamically consistent pseudo-inverse. Robotica 18(5):513–518
Nicolelis MA (2003) Brain-machine interfaces to restore motor function and probe neural circuits. Nat Rev Neurosci 4(5):417–422
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):43–50
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):1909–1916
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)
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):228–231
Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP (2004) Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J Neurophysiol 91(1):515–532
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):1–8
Santello M (2002) Kinematic synergies for the control of hand shape. Arch Italiennes de Biologie 140(3):221–228
Santhanam G, Ryu SI, Byron MY, Afshar A, Shenoy KV (2006) A high-performance brain–computer interface. Nature 442(7099):195–198
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):264
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):e9813
Seel N (2012) Operant conditioning. In: Seel N (ed) Encyclopedia of the sciences of learning. Springer, New York, p 2526
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):2353–2378
Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP (2002) Brain-machine interface: instant neural control of a movement signal. Nature 416(6877):141–142
Shadmehr R (2005) The computational neurobiology of reaching and pointing: a foundation for motor learning. MIT Press, Cambridge, MA
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, 21:1489–1496
Sitaram R, Caria A, Birbaumer N (2009) Hemodynamic brain-computer interfaces for communication and rehabilitation. Neural Netw 22(9):1320–1328
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):026004
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):315–332
Suminski AJ, Tkach DC, Hatsopoulos NG (2009) Exploiting multiple sensory modalities in brain-machine interfaces. Neural Netw 22(9):1224–1234
Suminski AJ, Tkach DC, Fagg AH, Hatsopoulos NG (2010) Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J Neurosci 30(50):16777–16787
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):367–376
Taylor DM, Tillery SIH, Schwartz AB (2002) Direct cortical control of 3d neuro-prosthetic devices. Science 296(5574):1829–1832
Tkach D, Reimer J, Hatsopoulos NG (2008) Observation-based learning for brain–machine interfaces. Curr Opin Neurobiol 18(6):589–594
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):10818–10823
Wang W, Chan SS, Heldman DA, Moran DW (2007) Motor cortical representation of position and velocity during reaching. J Neurophysiol 97(6):4258–4270
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):e55344
Wolpaw J, Wolpaw EW (2012) Brain-computer interfaces: principles and practice. Oxford University Press, New York
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):80–118
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Balasubramanian, K., Hatsopoulos, N.G. (2014). Cortical Motor Prosthesis. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_705-1
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