Key Points
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Brain–machine interface (BMI) control of the kinematics of reaching has progressed dramatically, whereas BMI control of the hand and of the dynamics of movement is still quite limited.
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Conveying somatosensory feedback is critical for BMIs to be clinically viable, but afferent interfaces are still rather primitive.
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Biomimicry — that is, attempting to exploit or reproduce natural patterns of neuronal activity — may be an important design criterion.
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Adaptation, the ability of the nervous system to adapt to novel motor and sensory mappings, is also likely to be crucial.
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The lifespan of cortical interfaces is currently inadequate.
Abstract
The loss of a limb or paralysis resulting from spinal cord injury has devastating consequences on quality of life. One approach to restoring lost sensory and motor abilities in amputees and patients with tetraplegia is to supply them with implants that provide a direct interface with the CNS. Such brain–machine interfaces might enable a patient to exert voluntary control over a prosthetic or robotic limb or over the electrically induced contractions of paralysed muscles. A parallel interface could convey sensory information about the consequences of these movements back to the patient. Recent developments in the algorithms that decode motor intention from neuronal activity and in approaches to convey sensory feedback by electrically stimulating neurons, using biomimetic and adaptation-based approaches, have shown the promise of invasive interfaces with sensorimotor cortices, although substantial challenges remain.
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References
Chapin, J. K., Moxon, K. A., Markowitz, R. S. & Nicolelis, M. A. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nature Neurosci. 2, 664–670 (1999).
Carmena, J. M. et al. Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol. 1, e42 (2003).
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1001 (2008).
Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. & Shenoy, K. V. A high-performance brain–computer interface. Nature 442, 195–198 (2006).
Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nature Neurosci. 15, 1752–1757 (2012).
Mulliken, G. H., Musallam, S. & Andersen, R. A. Decoding trajectories from posterior parietal cortex ensembles. J. Neurosci. 28, 12913–12926 (2008).
Li, Z., O'Doherty, J. E., Lebedev, M. A. & Nicolelis, M. A. Adaptive decoding for brain–machine interfaces through Bayesian parameter updates. Neural Comput. 23, 3162–3204 (2011).
Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012).
Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013). This study represents the current state of the art of a tetraplegic human controlling a prosthetic limb with many DOFs. Optimal performance was obtained through a two-stage biomimetic decoder and guided, progressive user adaptation.
Johansson, R. S. & Flanagan, J. R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nature Rev. Neurosci. 10, 345–359 (2009). This paper provides an excellent review of how tactile signals are important for manipulating objects.
London, B. M., Jordan, L. R., Jackson, C. R. & Miller, L. E. Electrical stimulation of the proprioceptive cortex (area 3a) used to instruct a behaving monkey. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 32–36 (2008).
O'Doherty, J. E., Lebedev, M. A., Li, Z. & Nicolelis, M. A. Virtual active touch using randomly patterned intracortical microstimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 85–93 (2012).
Venkatraman, S. & Carmena, J. M. Active sensing of target location encoded by cortical microstimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 317–3241 (2011).
Tabot, G. A. et al. Restoring the sense of touch with a prosthetic hand through a brain interface. Proc. Natl Acad. Sci. USA 110, 18279–18284 (2013). This study describes approaches to convey information about contact location, force and timing through a biomimetic strategy of ICMS applied to the somatosensory cortex.
Romo, R., Hernandez, A., Zainos, A., Brody, C. D. & Lemus, L. Sensing without touching: psychophysical performance based on cortical microstimulation. Neuron 26, 273–278 (2000).
Fitzsimmons, N. A., Drake, W., Hanson, T. L., Lebedev, M. A. & Nicolelis, M. A. Primate reaching cued by multichannel spatiotemporal cortical microstimulation. J. Neurosci. 27, 5593–5602 (2007).
Dadarlat, M. C., O'Doherty, J. E. & Sabes, P. N. Multisensory integration of vision and intracortical microstimulation for sensory substitution and augmentation. Soc. Neurosci. Abstr. 792.12 (2012).
Weber, D. J. et al. Limb-state information encoded by peripheral and central somatosensory neurons: Implications for an afferent interface. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 501–513 (2011).
Tomlinson, T., Ruiz Torres, R. & Miller, L. E. Multi-electrode stimulation in somatosensory area 2 induces a natural sensation of limb movement. Soc. Neurosci. Abstr. 835.03 (2013).
O'Doherty, J. E. et al. Active tactile exploration using a brain-machine-brain interface. Nature 479, 228–231 (2011). This study was the first to describe a bidirectional BMI in which a cursor was controlled by signals from the motor cortex while stimulation was delivered to the somatosensory cortex to signal the consequences of those movements. The monkey had to learn the mapping of the afferent interface.
Ethier, C., Oby, E. R., Bauman, M. J. & Miller, L. E. Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 485, 368–371 (2012). Monkeys were able to grasp and move objects despite temporary paralysis of forearm flexor muscles in this study, which used a biomimetic decoder to evoke electrically induced muscle contractions controlled in real-time by predictions of muscle activity.
Suminski, A. J., Willett, F. R., Fagg, A. H., Bodenhamer, M. & Hatsopoulos, N. G. Continuous decoding of intended movements with a hybrid kinetic and kinematic brain machine interface. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 5802–5806 (2011).
Chhatbar, P. Y. & Francis, J. T. Towards a naturalistic brain–machine interface: hybrid torque and position control allows generalization to novel dynamics. PLoS ONE 8, e52286 (2013).
Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. Instant neural control of a movement signal. Nature 416, 141–142 (2002). This classic study was the first to achieve continuous control of two-dimensional cursor movement using intracortical recordings of neural activity.
Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).
Taylor, D. M., Tillery, S. I. & Schwartz, A. B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).
Ganguly, K. & Carmena, J. M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 7, e1000153 (2009). Given several days of practice, monkeys became as proficient in using a decoder with scrambled inputs as they had been in its original, biomimetic state. They could readily switch between the two decoders. Changes in neural tuning tracked the behavioural improvement.
Jarosiewicz, B. et al. Functional network reorganization during learning in a brain–computer interface paradigm. Proc. Natl Acad. Sci. USA 105, 19486–19491 (2008). This study emulates the classic 'visual rotation' studies by systematically (rather than randomly) remapping the decoder output. It reveals a combination of global and local adaptive changes in neural tuning.
Ganguly, K., Dimitrov, D. F., Wallis, J. D. & Carmena, J. M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nature Neurosci. 14, 662–667 (2011).
Fetz, E. E. & Baker, M. A. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J. Neurophysiol. 36, 179–204 (1973). This paper extends earlier work on the conditioning of single neurons in M1 by showing that monkeys can learn to control the discharge of different neurons independently, even that of adjacent pairs of neurons.
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. 108, 624–644 (2012).
Millán, J. R. & Carmena, J. M. Invasive or noninvasive: understanding brain–machine interface technology. IEEE Eng. Med. Biol. Mag. 29, 16–19 (2010).
Barrese, J. C. et al. Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates. J. Neural Eng. 10, 066014 (2013).
Hwang, E. J. & Andersen, R. A. The utility of multichannel local field potentials for brain–machine interfaces. J. Neural Eng. 10, 046005 (2013).
Schalk, G. et al. Two-dimensional movement control using electrocorticographic signals in humans. J. Neural Eng. 5, 75–84 (2008).
Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc. Natl Acad. Sci. USA 101, 17849–17854 (2004).
Flint, R. D., Wright, Z. A., Scheid, M. R. & Slutzky, M. W. Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J. Neural Eng. 10, 056005 (2013).
Mehring, C. et al. Inference of hand movements from local field potentials in monkey motor cortex. Nature Neurosci. 6, 1253–1254 (2003).
Bansal, A. K., Vargas-Irwin, C. E., Truccolo, W. & Donoghue, J. P. Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices. J. Neurophysiol. 105, 1603–1619 (2011).
Stark, E. & Abeles, M. Predicting movement from multiunit activity. J. Neurosci. 27, 8387–8394 (2007).
Chestek, C. A. et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J. Neural Eng. 8, 045005 (2011).
Sellers, E., McFarland, D., Vaughan, T. & Wolpaw, J. in Brain–Computer Interfaces: Revolutionizing Human–Computer Interaction (eds Graimann, B., Allison, B. & Pfurtscheller, G.) 97–111 (Springer, 2010).
Wahnoun, R., He, J. & Helms Tillery, S. I. Selection and parameterization of cortical neurons for neuroprosthetic control. J. Neural Eng. 3, 162–171 (2006).
Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006). This study was the first in which a paralysed human patient achieved continuous, two-dimensional control of a cursor through an intracortical brain interface.
Tkach, D., Reimer, J. & Hatsopoulos, N. G. Congruent activity during action and action observation in motor cortex. J. Neurosci. 27, 13241–13250 (2007).
Rizzolatti, G. & Craighero, L. The mirror-neuron system. Annu. Rev. Neurosci. 27, 169–192 (2004).
di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V. & Rizzolatti, G. Understanding motor events: a neurophysiological study. Exp. Brain Res. 91, 176–180 (1992).
Cheney, P. D. & Fetz, E. E. Corticomotoneuronal cells contribute to long-latency stretch reflexes in the rhesus monkey. J. Physiol. 349, 249–272 (1984).
Asanuma, H. Functional role of sensory inputs to the motor cortex. Prog. Neurobiol. 16, 241–262 (1981).
Fetz, E. E., Perlmutter, S. I., Prut, Y., Seki, K. & Votaw, S. Roles of primate spinal interneurons in preparation and execution of voluntary hand movement. Brain Res. Rev. 40, 53–65 (2002).
Johannes, M. S. et al. An overview of the developmental process for the modular prosthetic limb. Johns Hopkins Apl. Tech. Digest 30, 207–216 (2011).
Landgren, S., Phillips, C. G. & Porter, R. Cortical fields of origin of the monosynaptic pyramidal pathways to some alpha motoneurones of the baboon's hand and forearm. J. Physiol. 161, 112–125 (1962).
Schieber, M. H. & Hibbard, L. S. How somatotopic is the motor cortex hand area? Science 261, 489–492 (1993).
Maier, M. A., Bennett, K. M. B., Hepp-Reymond, M. C. & Lemon, R. N. Contribution of the monkey corticomotoneuronal system to the control of force in precision grip. J. Neurophysiol. 69, 772–785 (1993).
Hendrix, C. M., Mason, C. R. & Ebner, T. J. Signaling of grasp dimension and grasp force in dorsal premotor cortex and primary motor cortex neurons during reach to grasp in the monkey. J. Neurophysiol. 102, 132–145 (2009).
Townsend, B. R., Subasi, E. & Scherberger, H. Grasp movement decoding from premotor and parietal cortex. J. Neurosci. 31, 14386–14398 (2011).
Carpaneto, J. et al. Continuous decoding of grasping tasks for a prospective implantable cortical neuroprosthesis. J. Neuroeng. Rehabil. 9, 84 (2012).
Egan, J., Baker, J., House, P. A. & Greger, B. Decoding dexterous finger movements in a neural prosthesis model approaching real-world conditions. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 836–844 (2012).
Aggarwal, V., Mollazadeh, M., Davidson, A. G., Schieber, M. H. & Thakor, N. V. State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements. J. Neurophysiol. 109, 3067–3081 (2013).
Vargas-Irwin, C. E. et al. Decoding complete reach and grasp actions from local primary motor cortex populations. J. Neurosci. 30, 9659–9669 (2010).
Santello, M., Flanders, M. & Soechting, J. F. Postural hand synergies for tool use. J. Neurosci. 18, 10105–10115 (1998).
Thakur, P. H., Bastian, A. J. & Hsiao, S. S. Multidigit movement synergies of the human hand in an unconstrained haptic exploration task. J. Neurosci. 28, 1271–1281 (2008).
Todorov, E. & Ghahramani, Z. Analysis of the synergies underlying complex hand manipulation. Conf. Proc. IEEE Eng. Med. Biol. Soc. 6, 4637–4640 (2004).
Thompson, D., Blain-Moraes, S. & Huggins, J. Performance assessment in brain-computer interface-based augmentative and alternative communication. BioMed Eng. Online 12, 43 (2013).
Yuan, P. et al. A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces. J. Neural Eng. 10, 026014 (2013).
Hogan, N. Active control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans. Automat. Control 29, 681–690 (1984).
Evarts, E. V. Relation of pyramidal tract activity to force exerted during voluntary movement. J. Neurophysiol. 31, 14–27 (1968).
Smith, A. M., Hepp-Reymond, M. C. & Wyss, U. R. Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles. Exp. Brain Res. 23, 315–332 (1975).
Kalaska, J. F., Cohen, D. A. D., Hyde, M. L. & Prud'homme, M. A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task. J. Neurosci. 9, 2080–2102 (1989).
Boline, J. & Ashe, J. On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional dynamic isometric force. Exp. Brain Res. 167, 148–159 (2005).
Cheney, P. D. & Fetz, E. E. Functional classes of primate corticomotorneuronal cells and their relation to active force. J. Neurophysiol. 44, 773–791 (1980).
Hepp-Reymond, M. C., Wyss, U. R. & Anner, R. Neuronal coding of static force in the primate motor cortex. J. Physiol. Paris 74, 287–291 (1978).
Humphrey, D. R., Schmidt, E. M. & Thompson, W. D. Predicting measures of motor performance from multiple cortical spike trains. Science 170, 758–761 (1970). This classic study was the first to make real-time predictions of movement-related parameters using single-unit recordings from multiple neurons in the motor cortex.
Fagg, A. H., Ojakangas, G. W., Miller, L. E. & Hatsopoulos, N. G. Kinetic trajectory decoding using motor cortical ensembles. IEEE Trans. Neural Syst. Rehabil. Eng. 17, 487–496 (2009).
Oby, E. R. et al. in Statistical Signal Processing for Neuroscience and Neurotechnology (ed. O'Weiss, K. G.) 369–406 (Academic Press, Elsevier, 2010).
Humphrey, D. R. & Reed, D. J. in Motor Control Mechanisms in Health and Disease (ed. Desmedt, J. E.) 347–372 (Raven, 1983).
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E. & Kawato, M. The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414, 446–449 (2001).
Franklin, D., Burdet, E., Osu, R., Kawato, M. & Milner, T. Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable dynamics. Exp. Brain Res. 151, 145–157 (2003).
Gribble, P. L., Mullin, L. I., Cothros, N. & Mattar, A. Role of cocontraction in arm movement accuracy. J. Neurophysiol. 89, 2396–2405 (2003).
Kim, H. K. et al. The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm. IEEE Trans. Biomed. Eng. 54, 1520–1529 (2007).
Pohlmeyer, E. A., Solla, S. A., Perreault, E. J. & Miller, L. E. Prediction of upper limb muscle activity from motor cortical discharge during reaching. J. Neural Eng. 4, 369–379 (2007).
Santucci, D. M., Kralik, J. D., Lebedev, M. A. & Nicolelis, M. A. Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates. Eur. J. Neurosci. 22, 1529–1540 (2005).
Flint, R. D., Ethier, C., Oby, E. R., Miller, L. E. & Slutzky, M. W. Local field potentials allow accurate decoding of muscle activity. J. Neurophysiol. 108, 18–24 (2012).
Pohlmeyer, E. A. et al. Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles. PLoS ONE 4, e5924 (2009).
Moritz, C. T., Perlmutter, S. I. & Fetz, E. E. Direct control of paralysed muscles by cortical neurons. Nature 456, 639–642 (2008).
Xerri, C., Merzenich, M. M., Jenkins, W. & Santucci, S. Representational plasticity in cortical area 3b paralleling tactual-motor skill acquisition in adult monkeys. Cereb. Cortex 9, 264–276 (1999).
Qi, H. X., Chen, L. M. & Kaas, J. H. Reorganization of somatosensory cortical areas 3b and 1 after unilateral section of dorsal columns of the spinal cord in squirrel monkeys. J. Neurosci. 31, 13662–13675 (2011).
Shadmehr, R. & Mussa-Ivaldi, F. A. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994).
Krakauer, J. W., Pine, Z. M., Ghilardi, M. F. & Ghez, C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 20, 8916–8924 (2000).
Cunningham, H. A. Aiming error under transformed spatial mappings suggests a structure for visual-motor maps. J. Exp. Psychol. Hum. Percept. Perform. 15, 493–506 (1989).
Ostry, D. J., Darainy, M., Mattar, A. A., Wong, J. & Gribble, P. L. Somatosensory plasticity and motor learning. J. Neurosci. 30, 5384–5393 (2010).
Cressman, E. K. & Henriques, D. Y. P. Sensory recalibration of hand position following visuomotor adaptation. J. Neurophysiol. 102, 3505–3518 (2009).
Nasir, S. M., Darainy, M. & Ostry, D. J. Sensorimotor adaptation changes the neural coding of somatosensory stimuli. J. Neurophysiol. 109, 2077–2085 (2013).
Lebedev, M. A. et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J. Neurosci. 25, 4681–4693 (2005).
Fetz, E. E. & Finocchio, D. V. Operant conditioning of specific patterns of neural and muscular activity. Science 174, 431–435 (1971).
Moritz, C. T. & Fetz, E. E. Volitional control of single cortical neurons in a brain-machine interface. J. Neural Eng. 8, 025017 (2011).
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. 8, 198–202 (2000).
Ochoa, J. & Torebjork, E. Sensations evoked by intraneural microstimulation of single mechanoreceptor units innervating the human hand. J. Physiol. 342, 633–654 (1983).
Wheat, H. E., Goodwin, A. W. & Browning, A. S. Tactile resolution: peripheral neural mechanisms underlying the human capacity to determine positions of objects contacting the fingerpad. J. Neurosci. 15, 5582–5595 (1995).
Knibestol, M. Stimulus-response functions of slowly adapting mechanoreceptors in the human glabrous skin area. J. Physiol. 245, 63–80 (1975).
Macefield, V. G., Hager-Ross, C. & Johansson, R. S. Control of grip force during restraint of an object held between finger and thumb: responses of cutaneous afferents from the digits. Exp. Brain Res. 108, 155–171 (1996).
Goodwin, A. W. & Wheat, H. E. Sensory signals in neural populations underlying tactile perception and manipulation. Annu. Rev. Neurosci. 27, 53–77 (2004).
Johansson, R. S. & Westling, G. Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Exp. Brain Res. 56, 550–564 (1984).
Monzée, J., Lamarre, Y. & Smith, A. M. The effects of digital anesthesia on force control using a precision grip. J. Neurophysiol. 89, 672–683 (2003).
Brochier, T., Boudreau, M. J., Paré, M. & Smith, A. M. The effects of muscimol inactivation of small regions of motor and somatosensory cortex on independent finger movements and force control in the precision grip. Exp. Brain Res. 128, 31–40 (1999).
Sainburg, R. L., Ghilardi, M. F., Poizner, H. & Ghez, C. Control of limb dynamics in normal subjects and patients without proprioception. J. Neurophysiol. 73, 820–835 (1995).
Botvinick, M. & Cohen, J. Rubber hands “feel” touch that eyes see. Nature 391, 756 (1998).
Dunbar, R. I. The social role of touch in humans and primates: behavioural function and neurobiological mechanisms. Neurosci. Biobehav. Rev. 34, 260–268 (2010).
Blabe, C. et al. Assessing the brain–machine interface priiorities from the perspective of spinal cord injury participants. Soc. Neurosci. Abstr. 584.14 (2012).
Penfield, W. & Boldrey, E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60, 389–443 (1937).
Rasmussen, T. & Penfield, W. The human sensorimotor cortex as studied by electrical stimulation. Fed. Proc. 6, 184 (1947).
Romo, R., Hernandez, A., Zainos, A. & Salinas, E. Somatosensory discrimination based on cortical microstimulation. Nature 392, 387–390 (1998). In this landmark study, monkeys were trained to discriminate the frequencies of sequential mechanical vibrations applied to the fingertip. They were then able to discriminate the frequency of ICMS pulse trains delivered to S1 when substituted for one or both of the mechanical stimuli.
Fridman, G., Blair, H., Blaisdell, A. & Judy, J. Perceived intensity of somatosensory cortical electrical stimulation. Exp. Brain Res. 203, 499–515 (2010).
Semprini, M., Bennicelli, L. & Vato, A. A parametric study of intracortical microstimulation in behaving rats for the development of artificial sensory channels. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2012, 799–802 (2012).
Zaaimi, B., Ruiz-Torres, R., Solla, S. A. & Miller, L. E. Multi-electrode stimulation in somatosensory cortex increases probability of detection. J. Neural Engineer. 10, 056013 (2013).
O'Connor, D. H. et al. Neural coding during active somatosensation revealed using illusory touch. Nature Neurosci. 16, 958–965 (2013).
Ramachandran, V. S. & Hirstein, W. The perception of phantom limbs. The D. O. Hebb lecture. Brain 121, 1603–1630 (1998). This paper provides an overview of the phenomenon of the phantom limb, which in turn sheds light on how somatosensory representations evolve after deafferentation.
Berg, J. A. et al. Behavioral demonstration of a somatosensory neuroprosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 500–507 (2013).
Pei, Y. C., Hsiao, S. S., Craig, J. C. & Bensmaia, S. J. Shape invariant coding of motion direction in somatosensory cortex. PLoS Biol. 8, e1000305 (2010).
Bensmaia, S. J., Denchev, P. V., Dammann, J. F., Craig, J. C. & Hsiao, S. S. The representation of stimulus orientation in the early stages of somatosensory processing. J. Neurosci. 28, 776–786 (2008).
Costanzo, R. M. & Gardner, E. P. Multiple-joint neurons in somatosensory cortex of awake monkeys. Brain Res. 214, 321–333 (1981).
Prud'homme, M. J. L. & Kalaska, J. F. Proprioceptive activity in primate primary somatosensory cortex during active arm reaching movements. J. Neurophysiol. 72, 2280–2301 (1994).
London, B. M. & Miller, L. E. Responses of somatosensory area 2 neurons to actively and passively generated limb movements. J. Neurophysiol. 109, 1505–1513 (2013).
Histed, M. H., Bonin, V. & Reid, R. C. Direct activation of sparse, distributed populations of cortical neurons by electrical microstimulation. Neuron 63, 508–522 (2009).
Logothetis, N. K. et al. The effects of electrical microstimulation on cortical signal propagation. Nature Neurosci. 13, 1283–1291 (2010).
Mountcastle, V. B. & Powell, T. P. Central nervous mechanisms subserving position sense and kinesthesis. Bull. Johns Hopkins Hosp. 105, 173–200 (1959).
Diester, I. et al. An optogenetic toolbox designed for primates. Nature Neurosci. 14, 387–397 (2011).
Cardin, J. A. et al. Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2. Nature Protoc. 5, 247–254 (2010).
Thomson, E. E., Carra, R. & Nicolelis, M. A. Perceiving invisible light through a somatosensory cortical prosthesis. Nature Commun. 4, 1482 (2013).
Marasco, P. D., Schultz, A. E. & Kuiken, T. A. Sensory capacity of reinnervated skin after redirection of amputated upper limb nerves to the chest. Brain 132, 1441–1448 (2009).
Marasco, P. D., Kim, K., Colgate, J. E., Peshkin, M. A. & Kuiken, T. A. Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees. Brain 134, 747–758 (2011).
Shokur, S. et al. Expanding the primate body schema in sensorimotor cortex by virtual touches of an avatar. Proc. Natl Acad. Sci. USA 110, 15121–15126 (2013).
Ince, N. F. et al. High accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials. PLoS ONE 5, e14384 (2010).
Rickert, J. et al. Encoding of movement direction in different frequency ranges of motor cortical local field potentials. J. Neurosci. 25, 8815–8824 (2005).
Andersen, R. A., Musallam, S. & Pesaran, B. Selecting the signals for a brain-machine interface. Curr. Opin. Neurobiol. 14, 720–726 (2004).
Markowitz, D. A., Wong, Y. T., Gray, C. M. & Pesaran, B. Optimizing the decoding of movement goals from local field potentials in macaque cortex. J. Neurosci. 31, 18412–18422 (2011).
Pesaran, B., Pezaris, J. S., Sahani, M., Mitra, P. P. & Andersen, R. A. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nature Neurosci. 5, 805–811 (2002).
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. 25, 1693–1731 (2013).
Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nature Rev. Neurosci. 13, 407–420 (2012).
Kim, S. P. et al. Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models. Neural Netw. 16, 865–871 (2003).
Kim, S. P. et al. A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces. J. Neural Eng. 3, 145–161 (2006).
Shoham, S. et al. Statistical encoding model for a primary motor cortical brain-machine interface. IEEE Trans. Biomed. Eng. 52, 1312–1322 (2005).
Yu, B. M. et al. Mixture of trajectory models for neural decoding of goal-directed movements. J. Neurophysiol. 97, 3763–3780 (2007).
Yu, B., Cunningham, J., Shenoy, K. & Sahani, M. in Neural Information Processing; Lecture Notes in Computer Science Vol. 4984 (eds Ishikawa, M., Doya, K., Miyamoto, H. & Yamakawa, T.) 586–595 (Springer, 2008).
Palmer, S. S. & Fetz, E. E. Discharge properties of prima te forearm motor units during isometric muscle activity. J. Neurophysiol. 54, 1178–1193 (1985).
Sergio, L. E., Hamel-Paquet, C. & Kalaska, J. F. Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks. J. Neurophysiol. 94, 2353–2378 (2005).
Fetz, E. E. Are movement parameters recognizably coded in activity of single neurons? Behav. Brain Sci. 15, 679–690 (1992).
Phillips, C. G. Laying the ghost of 'muscles versus movements'. Can. J. Neurol. Sci. 2, 209–218 (1975).
Scott, S. H. & Kalaska, J. F. Reaching movements with similar hand paths but different arm orientations. I. Activity of individual cells in motor cortex. J. Neurophysiol. 77, 826–852 (1997).
Caminiti, R., Johnson, P. B. & Urbano, A. Making arm movements within different parts of space: dynamic aspects in the primate motor cortex. J. Neurosci. 10, 2039–2058 (1990).
Kakei, S., Hoffman, D. S. & Strick, P. L. Muscle and movement representations in the primary motor cortex. Science 285, 2136–2139 (1999).
Oby, E. R., Ethier, C. & Miller, L. E. Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions. J. Neurophysiol. 109, 666–678 (2013).
Cherian, A., Krucoff, M. O. & Miller, L. E. Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics. J. Neurophysiol. 106, 564–575 (2011).
Gupta, R. & Ashe, J. Lack of adaptation to random conflicting force fields of variable magnitude. J. Neurophysiol. 97, 738–745 (2007).
Hatsopoulos, N., Joshi, J. & O'Leary, J. G. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. J. Neurophysiol. 92, 1165–1174 (2004).
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 109, 17075–17080 (2012).
Pesaran, B., Nelson, M. J. & Andersen, R. A. Dorsal premotor neurons encode the relative position of the hand, eye, and goal during reach planning. Neuron 51, 125–134 (2006).
Musallam, S., Corneil, B. D., Greger, B., Scherberger, H. & Andersen, R. A. Cognitive control signals for neural prosthetics. Science 305, 258–262 (2004).
Ganguly, K. & Carmena, J. M. Neural correlates of skill acquisition with a cortical brain-machine interface. J. Motor Behav. 42, 355–360 (2010).
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 (2010).
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. 20, 468–477 (2012).
Fishbach, A., Roy, S., Bastianen, C., Miller, L. & Houk, J. Kinematic properties of on-line error corrections in the monkey. Exp. Brain Res. 164, 442–457 (2005).
Danziger, Z., Fishbach, A. & Mussa-Ivaldi, F. A. Learning algorithms for human–machine interfaces. IEEE Trans. Biomed. Eng. 56, 1502–1511 (2009).
Mahmoudi, B. & Sanchez, J. C. A. Symbiotic brain-machine interface through value-based decision making. PLoS ONE 6, e14760 (2011).
Mahmoudi, B., Pohlmeyer, E. A., Prins, N. W., Geng, S. & Sanchez, J. C. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning. J. Neural Eng. 10, 066005 (2013).
Judy, J. W. Neural interfaces for upper-limb prosthesis control: opportunities to improve long-term reliability. IEEE Pulse 3, 57–60 (2012).
Perge, J. A. et al. Intra-day signal instabilities affect decoding performance in an intracortical neural interface system. J. Neural Eng. 10, 036004 (2013).
Polikov, V. S., Tresco, P. A. & Reichert, W. M. Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods 148, 1–18 (2005).
Prasad, A. et al. Comprehensive characterization and failure modes of tungsten microwire arrays in chronic neural implants. J. Neural Eng. 9, 056015 (2012).
McCreery, D., Pikov, V. & Troyk, P. Neuronal loss due to prolonged controlled-current stimulation with chronically implanted microelectrodes in the cat cerebral cortex. J. Neural Eng. 7, 036005 (2010).
Parker, R. A., Davis, T. S., House, P. A., Normann, R. A. & Greger, B. The functional consequences of chronic, physiologically effective intracortical microstimulation. Prog. Brain Res. 194, 145–165 (2011).
Chen, K. H. et al. The effect of chronic intracortical microstimulation on the electrode–tissue interface. J. Neural Eng. 11, 026004 (2014).
Kane, S. et al. Electrical performance of penetrating microelectrodes chronically implanted in cat cortex. IEEE Trans. Biomed. Eng. 60, 2153–2160 (2013).
Cogan, S. F. Neural stimulation and recording electrodes. Annu. Rev. Biomed. Eng. 10, 275–309 (2008).
Simeral, J. D. et al. Some preliminary longitudinal findings from five trial participants using the BrainGate neural interface system. Soc. Neurosci. Abstr. 142.04 (2011).
Grill, W. M., Norman, S. E. & Bellamkonda, R. V. Implanted neural interfaces: biochallenges and engineered solutions. Annu. Rev. Biomed. Eng. 11, 1–24 (2009).
Reichert, W. M. Indwelling Neural Implants: Strategies for Contending with the In Vivo Environment (CRC, 2010).
Sridharan, A., Rajan, S. D. & Muthuswamy, J. Long-term changes in the material properties of brain tissue at the implant–tissue interface. J. Neural Eng. 10, 066001 (2013).
Harris, J. P. et al. In vivo deployment of mechanically adaptive nanocomposites for intracortical microelectrodes. J. Neural Eng. 8, 046010 (2011).
Ware, T., Simon, D., Rennaker, R. L. & Voit, W. Smart polymers for neural interfaces. Polymer Rev. 53, 108–129 (2013).
Capadona, J. R., Tyler, D. J., Zorman, C. A., Rowan, S. J. & Weder, C. Mechanically adaptive nanocomposites for neural interfacing. MRS Bull. 37, 581–589 (2012).
Lind, G., Linsmeier, C. E., Thelin, J. & Schouenborg, J. Gelatine-embedded electrodes—a novel biocompatible vehicle allowing implantation of highly flexible microelectrodes. J. Neural Engineer. 7, 046005 (2010).
Khodagholy, D. et al. Highly conformable conducting polymer electrodes for in vivo recordings. Adv. Mater. 23, H268–H272 (2011).
Kim, T., Branner, A., Gulati, T. & Giszter, S. F. Braided multi-electrode probes: mechanical compliance characteristics and recordings from spinal cords. J. Neural Eng. 10, 045001 (2013).
Rousche, P. J. et al. Flexible polyimide-based intracortical electrode arrays with bioactive capability. IEEE Trans. Biomed. Eng. 48, 361–371 (2001).
Moxon, K. A. et al. Nanostructured surface modification of ceramic-based microelectrodes to enhance biocompatibility for a direct brain-machine interface. IEEE Trans. Biomed. Eng. 51, 881–889 (2004).
Kim, D.-H. & Martin, D. C. Sustained release of dexamethasone from hydrophilic matrices using PLGA nanoparticles for neural drug delivery. Biomaterials 27, 3031–3037 (2006).
Cho, Y. & Ben Borgens, R. Electrically controlled release of the nerve growth factor from a collagen-carbon nanotube composite for supporting neuronal growth. J. Mater. Chem. B 1, 4166–4170 (2013).
Keefer, E. W., Botterman, B. R., Romero, M. I., Rossi, A. F. & Gross, G. W. Carbon nanotube coating improves neuronal recordings. Nature Nanotechnol. 3, 434–439 (2008).
Kotov, N. A. et al. Nanomaterials for Neural Interfaces. Adv. Mater. 21, 3970–4004 (2009).
Stauffer, W. R. & Cui, X. T. Polypyrrole doped with 2 peptide sequences from laminin. Biomaterials 27, 2405–2413 (2006).
Malarkey, E. & Parpura, V. in Brain Edema XIV Vol. 106 Acta Neurochirurgica Supplementum (eds Czernicki, Z. et al.) 337–341 (Springer, 2010).
Kenney, C. et al. Short-term and long-term safety of deep brain stimulation in the treatment of movement disorders. J. Neurosurg. 106, 621–625 (2007).
Rizk, M. et al. A fully implantable 96-channel neural data acquisition system. J. Neural Eng. 6, 026002 (2009).
Zhang, F., Aghagolzadeh, M. & Oweiss, K. A. Fully implantable, programmable and multimodal neuroprocessor for wireless, cortically controlled brain-machine interface applications. J. Signal Process. Syst. 69, 351–361 (2012).
Borton, D. A., Yin, M., Aceros, J. & Nurmikko, A. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J. Neural Eng. 10, 026010 (2013).
Harrison, R. R. The design of integrated circuits to observe brain activity. Proc. IEEE 96, 1203–1216 (2008).
Acknowledgements
The authors gratefully thank J. Yau, H. Saal, A. Suminski and K. Otto for their comments on a previous version of the manuscript. The authors also thank G. Tabot for designing figure 1. S.J.B.is supported by US Defense Advanced Research Projects Agency (DARPA) contract #N66001-10-C-4056, US National Science Foundation (NSF) grant IOS-1150209 and US National Institutes of Health (NIH) grant 082865. L.E.M. is supported by grants from the US NIH (NS053603, NS048845) and the US NSF (0932263), with additional funding from the Chicago Community Trust through the Searle Program for Neurological Restoration.
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Glossary
- Degree of freedom
-
(DOF). The number of signals required to control a device. The DOF is determined approximately by the number of parameters that defines its configuration.
- Decoders
-
A set of (often linear) coefficients used to transform a large number of signals recorded from the brain into a small number of control signals. A decoder might also be used simply to classify the brain signals into two or more clusters that could be used to control the state of a limb.
- Offline analysis
-
A test of decoder performance, typically using signals previously recorded from an able-bodied subject, enabling comparison of the decoder's 'predictions' with the actual movement-related signals.
- Impedance
-
In electricity, the opposition to alternating current by an electric circuit. In limb movement, a measure of how much the limb resists motion when subjected to a force.
- Redundant
-
A limb having more degrees of freedom (for example, muscles or joint rotations) than are minimally necessary to position and orient its end point. Redundancy conveys flexibility but also requires more complex control algorithms.
- Actor–critic
-
A reinforcement learning approach that consists of having an 'actor' perform an action based on the state of the system and a 'critic' evaluate the consequences of that action. The probability of performing that action given the state is then modified based on the consequences.
- Online control
-
Actual predictions made with a decoder in real-time, allowing the user to control a robotic limb or the motion of a cursor.
- Ballistic
-
A preprogrammed movement that is sufficiently rapid that it cannot be modified by online sensory feedback.
- Proprioception
-
The sense of the relative position and motion of parts of the body (particularly limbs) and of the effort deployed in movement.
- Flutter
-
Low-frequency (∼5–50 Hz) oscillations.
- Verisimilitude
-
In the context of sensory brain–machine interfaces, the similarity to naturally occurring percepts.
- Percutaneous
-
Literally, 'by way of the skin'. In this context, an interface that penetrates the skin in order to convey signals to and from the nervous system.
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Bensmaia, S., Miller, L. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat Rev Neurosci 15, 313–325 (2014). https://doi.org/10.1038/nrn3724
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DOI: https://doi.org/10.1038/nrn3724
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