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. 2008 Jan;5(1):137–146. doi: 10.1016/j.nurt.2007.11.002

The development of brain-machine interface neuroprosthetic devices

Parag G Patil 1,, Dennis A Turner 3
PMCID: PMC5084136  PMID: 18164493

Summary

The development of brain-machine interface technology is a logical next step in the overall direction of neuro-prosthetics. Many of the required technological advances that will be required for clinical translation of brain-machine interfaces are already under development, including a new generation of recording electrodes, the decoding and interpretation of signals underlying intention and planning, actuators for implementation of mental plans in virtual or real contexts, direct somatosensory feedback to the nervous system to refine actions, and training to encourage plasticity in neural circuits. Although pre-clinical studies in nonhuman primates demonstrate high efficacy in a realistic motor task with motor cortical recordings, there are many challenges in the clinical translation of even simple tasks and devices. Foremost among these challenges is the development of biocompatible electrodes capable of long-term, stable recording of brain activity and implantable amplifiers and signal processors that are sufficiently resistant to noise and artifact to faithfully transmit recorded signals to the external environment. Whether there is a suitable market for such new technology depends on its efficacy in restoring and enhancing neural function, its risks of implantation, and its long-term efficacy and usefulness. Now is a critical time in brain-machine interface development because most ongoing studies are science-based and noncommercial, allowing new approaches to be included in commercial schemes under development.

Key Words: Brain-machine interface, brain-computer interface, prosthesis, electrode, EEG

References

  • 1.Schwartz AB, Cui XT, Weber DJ, Moran DW. Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron. 2006;52:205–220. doi: 10.1016/j.neuron.2006.09.019. [DOI] [PubMed] [Google Scholar]
  • 2.Leuthardt EC, Schalk G, Moran D, Ojemann JG. The emerging world of motor neuroprosthetics: a neurosurgical perspective. Neurosurgery. 2006;59:1–14. doi: 10.1227/01.NEU.0000221506.06947.AC. [DOI] [PubMed] [Google Scholar]
  • 3.Lebedev MA, Nicolelis MA. Brain-machine interfaces: past, present and future. Trends Neurosci. 2006;29:536–546. doi: 10.1016/j.tins.2006.07.004. [DOI] [PubMed] [Google Scholar]
  • 4.Donoghue JP, Nurmikko A, Black M, Hochberg LR. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J Physiol. 2001;409:403–407. doi: 10.1113/jphysiol.2006.127209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Nicolelis MA. Actions from thoughts. Nature. 2001;409:403–407. doi: 10.1038/35053191. [DOI] [PubMed] [Google Scholar]
  • 6.Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006;442:164–171. doi: 10.1038/nature04970. [DOI] [PubMed] [Google Scholar]
  • 7.Taylor DM, Tillery SI, Schwartz AB. Direct cortical control of 3D neuroprosthetic devices. Science. 2002;296:1829–1832. doi: 10.1126/science.1070291. [DOI] [PubMed] [Google Scholar]
  • 8.Carmena JM, Lebedev MA, Crist RE, et al. Learning to control a brain-machine interface for reaching and grasping by primates. Plos Biology. 2003;1:193–208. doi: 10.1371/journal.pbio.0000042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proceedings of the National Academy of Sciences of the United States of America. 2004;101:17849–17854. doi: 10.1073/pnas.0403504101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Birbaumer N, Kubler A, Ghanayim N, et al. The thought translation device (TTD) for completely paralyzed patients. IEEE Trans Rehab Eng. 2000;8:190–193. doi: 10.1109/86.847812. [DOI] [PubMed] [Google Scholar]
  • 11.Birbaumer N, Hinterberger T, Kubler A, Neumann N. The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans Neural Syst Rehabil Eng. 2003;11:120–123. doi: 10.1109/TNSRE.2003.814439. [DOI] [PubMed] [Google Scholar]
  • 12.Kennedy PR, Bakay RA, Moore MM, Adams K, Goldwaithe J. Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng. 2000;8:198–202. doi: 10.1109/86.847815. [DOI] [PubMed] [Google Scholar]
  • 13.Peckham PH, Keith MW, Kilgore KL, et al. Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: a multicenter study. Arch Phys Med Rehabil. 2001;82:1380–1388. doi: 10.1053/apmr.2001.25910. [DOI] [PubMed] [Google Scholar]
  • 14.Kipke DR. Implantable neural probe systems for cortical neuro-prostheses. Conf Proc IEEE Eng Med Biol Soc. 2004;7:5344–5347. doi: 10.1109/IEMBS.2004.1404492. [DOI] [PubMed] [Google Scholar]
  • 15.Kralik JD, Dimitrov DF, Krupa DJ, Katz DB, Cohen D, Nicolelis MA. Techniques for long-term multisite neuronal ensemble recordings in behaving animals. Methods. 2001;25:121–150. doi: 10.1006/meth.2001.1231. [DOI] [PubMed] [Google Scholar]
  • 16.Rousche PJ, Normann RA. Chronic recording capability of the Utah intracortical electrode array in cat sensory cortex. J Neurosci Methods. 1998;82:1–15. doi: 10.1016/S0165-0270(98)00031-4. [DOI] [PubMed] [Google Scholar]
  • 17.Vetter RJ, Williams JC, Hetke JF, Nunamaker EA, Kipke DR. Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex. IEEE Trans Biomed Eng. 2004;51:896–904. doi: 10.1109/TBME.2004.826680. [DOI] [PubMed] [Google Scholar]
  • 18.Kennedy PR, Kirby MT, Moore MM, King B, Mallory A. Computer control using human intracortical local field potentials. IEEE Trans Neural Syst Rehab Eng. 2004;12:339–344. doi: 10.1109/TNSRE.2004.834629. [DOI] [PubMed] [Google Scholar]
  • 19.Kennedy PR, Bakay RAE, Moore MM, Adams K, Goldwaithe J. Direct control of a computer from the human central nervous system. IEEE Trans Rehab Eng. 2000;8:198–202. doi: 10.1109/86.847815. [DOI] [PubMed] [Google Scholar]
  • 20.Kennedy PR, Bakay RAE. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport. 1998;9:1707–1711. doi: 10.1097/00001756-199806010-00007. [DOI] [PubMed] [Google Scholar]
  • 21.Nguyen-Vu TDB, Chen H, Cassell AM, Andrews RJ, Meyyappan M, Li J. Vertically aligned carbon nanofiber architecture as a multifunctional 3-D neural electrical interface. IEEE Trans Biomed Eng. 2007;54:1121–1128. doi: 10.1109/TBME.2007.891169. [DOI] [PubMed] [Google Scholar]
  • 22.Georgopoulos AP, Kettner RE, Schwartz AB. Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J Neurosci. 1988;8:2928–2937. doi: 10.1523/JNEUROSCI.08-08-02928.1988. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Sanchez JC, Principe JC, Carmena JM, Lebedev MA, Nicolelis MA. Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network. Conf Proc IEEE Eng Med Biol Soc. 2004;7:5321–5324. doi: 10.1109/IEMBS.2004.1404486. [DOI] [PubMed] [Google Scholar]
  • 24.Fetz EE, Finocchio DV. Operant conditioning of specific patterns of neural and muscular activity. Science. 1971;174:431–435. doi: 10.1126/science.174.4007.431. [DOI] [PubMed] [Google Scholar]
  • 25.Fetz EE. Real-time control of a robotic arm by neuronal ensembles. Nat Neurosci. 1999;2:583–584. doi: 10.1038/10131. [DOI] [PubMed] [Google Scholar]
  • 26.Chapin JK, Moxon KA, Markowitz RS, Nicolelis MAL. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci. 1999;2:664–670. doi: 10.1038/10223. [DOI] [PubMed] [Google Scholar]
  • 27.Wessberg J, Stambaugh CR, Kralik JD, et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature. 2000;408:361–365. doi: 10.1038/35042582. [DOI] [PubMed] [Google Scholar]
  • 28.Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP. Instant neural control of a movement signal. Nature. 2002;416:141–142. doi: 10.1038/416141a. [DOI] [PubMed] [Google Scholar]
  • 29.Kennedy PR, Bakay RA. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport. 1998;9:1707–1711. doi: 10.1097/00001756-199806010-00007. [DOI] [PubMed] [Google Scholar]
  • 30.Kennedy PR. The cone electrode—a long-term electrode that records from neurites grown onto its recording surface. J Neurosci Methods. 1989;29:181–193. doi: 10.1016/0165-0270(89)90142-8. [DOI] [PubMed] [Google Scholar]
  • 31.Patil PG, Carmena JM, Nicolelis MA, Turner DA. Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface. Neurosurgery. 2004;55:27–35. [PubMed] [Google Scholar]
  • 32.Leuthardt EC, Miller KJ, Schalk G, Rao RP, Ojemann JG. Electrocorticography-based brain computer interface—the Seattle experience. IEEE Trans Neural Syst Rehabil Eng. 2006;14:194–198. doi: 10.1109/TNSRE.2006.875536. [DOI] [PubMed] [Google Scholar]
  • 33.Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW. A brain-computer interface using electrocorticographic signals in humans. J Neural Eng. 2004;1:63–71. doi: 10.1088/1741-2560/1/2/001. [DOI] [PubMed] [Google Scholar]
  • 34.Birbaumer N, Ghanayim N, Hinterberger T, et al. A spelling device for the paralysed. Nature. 1999;398:297–298. doi: 10.1038/18581. [DOI] [PubMed] [Google Scholar]
  • 35.Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett. 1997;239:65–68. doi: 10.1016/S0304-3940(97)00889-6. [DOI] [PubMed] [Google Scholar]
  • 36.Pfurtscheller G, Guger C, Muller G, Krausz G, Neuper C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci Lett. 2000;292:211–214. doi: 10.1016/S0304-3940(00)01471-3. [DOI] [PubMed] [Google Scholar]
  • 37.Pfurtscheller G, Muller GR, Pfurtscheller J, Gerner HJ, Rupp R. “Thought”—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett. 2003;351:33–36. doi: 10.1016/S0304-3940(03)00947-9. [DOI] [PubMed] [Google Scholar]
  • 38.Muller-Putz GR, Scherer R, Pfurtscheller G, Rupp R. EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci Lett. 2005;382:169–174. doi: 10.1016/j.neulet.2005.03.021. [DOI] [PubMed] [Google Scholar]
  • 39.Obermaier B, Muller GR, Pfurtscheller G. “Virtual keyboard” controlled by spontaneous EEG activity. IEEE Trans Neural Syst Rehabil Eng. 2003;11:422–426. doi: 10.1109/TNSRE.2003.816866. [DOI] [PubMed] [Google Scholar]
  • 40.Wolpaw JR. Brain-computer interfaces as new brain output pathways. J Physiol. 2007;579:613–619. doi: 10.1113/jphysiol.2006.125948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Buneo CA, Andersen RA. The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. Neuropsychologia. 2006;44:2594–2606. doi: 10.1016/j.neuropsychologia.2005.10.011. [DOI] [PubMed] [Google Scholar]
  • 42.Davis KD, Kiss ZH, Luo L, Tasker RR, Lozano AM, Dostrovsky JO. Phantom sensations generated by thalamic microstimulation. Nature. 1998;391:385–387. doi: 10.1038/34905. [DOI] [PubMed] [Google Scholar]
  • 43.Fitzsimmons NA, Drake W, Hanson TL, Lebedev MA, Nicolelis MA. Primate reaching cued by multichannel spatiotemporal cortical microstimulation. J Neurosci. 2007;27:5593–5602. doi: 10.1523/JNEUROSCI.5297-06.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Fetz EE. Operant conditioning of cortical unit activity. Science. 1969;163:955–958. doi: 10.1126/science.163.3870.955. [DOI] [PubMed] [Google Scholar]
  • 45.Gage GJ, Ludwig KA, Otto KJ, et al. Naive coadaptive cortical control. J Neural Eng. 2005;2:52–63. doi: 10.1088/1741-2560/2/2/006. [DOI] [PubMed] [Google Scholar]
  • 46.Lebedev MA, Carmena JM, O’Doherty JE, et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J Neurosci. 2005;25:4681–4693. doi: 10.1523/JNEUROSCI.4088-04.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Andersen RA, Musallam S, Pesaran B. Selecting the signals for a brain-machine interface. Curr Opin Neurobiol. 2004;14:720–726. doi: 10.1016/j.conb.2004.10.005. [DOI] [PubMed] [Google Scholar]
  • 48.Donoghue JP, Hochberg LR, Nurmikko AV, Black MJ, Simeral JD, Friehs G. Neuromotor prosthesis development. Med Health R I. 2007;90:12–15. [PubMed] [Google Scholar]
  • 49.Laubach M, Wessberg J, Nicolelis MA. Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature. 2000;405:567–571. doi: 10.1038/35014604. [DOI] [PubMed] [Google Scholar]
  • 50.Moxon KA, Leiser SC, Gerhardt GA, et al. Ceramic-based multisite electrode arrays for chronic single-neuron recording. IEEE Trans Biomed Eng. 2004;51:647–656. doi: 10.1109/TBME.2003.821037. [DOI] [PubMed] [Google Scholar]
  • 51.Birbaumer N. Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control. Psychophysiology. 2006;43:517–532. doi: 10.1111/j.1469-8986.2006.00456.x. [DOI] [PubMed] [Google Scholar]
  • 52.Birbaumer N, Cohen LG. Brain-computer interfaces: communication and restoration of movement in paralysis. J Physiol. 2007;579:621–636. doi: 10.1113/jphysiol.2006.125633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Birbaumer N, Weber C, Neuper C, Buch E, Haapen K, Cohen L. Physiological regulation of thinking: brain-computer interface (BCI) research. Prog Brain Res. 2006;159:369–391. doi: 10.1016/S0079-6123(06)59024-7. [DOI] [PubMed] [Google Scholar]
  • 54.Kiss ZH, Davis KD, Tasker RR, Lozano AM, Hu B, Dostrovsky JO. Kinaesthetic neurons in thalamus of humans with and without tremor. Exp Brain Res. 2003;150:85–94. doi: 10.1007/s00221-003-1399-3. [DOI] [PubMed] [Google Scholar]

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