Connect
MJA
MJA

Neurobionics and the brain–computer interface: current applications and future horizons

Jeffrey V Rosenfeld and Yan Tat Wong
Med J Aust 2017; 206 (8): 363-368. || doi: 10.5694/mja16.01011

Summary

  • The brain–computer interface (BCI) is an exciting advance in neuroscience and engineering.
  • In a motor BCI, electrical recordings from the motor cortex of paralysed humans are decoded by a computer and used to drive robotic arms or to restore movement in a paralysed hand by stimulating the muscles in the forearm. Simultaneously integrating a BCI with the sensory cortex will further enhance dexterity and fine control.
  • BCIs are also being developed to:
    • provide ambulation for paraplegic patients through controlling robotic exoskeletons;
    • restore vision in people with acquired blindness;
    • detect and control epileptic seizures; and
    • improve control of movement disorders and memory enhancement.
  • High-fidelity connectivity with small groups of neurons requires microelectrode placement in the cerebral cortex. Electrodes placed on the cortical surface are less invasive but produce inferior fidelity. Scalp surface recording using electroencephalography is much less precise.
  • BCI technology is still in an early phase of development and awaits further technical improvements and larger multicentre clinical trials before wider clinical application and impact on the care of people with disabilities. There are also many ethical challenges to explore as this technology evolves.

Please login with your free MJA account to view this article in full

  • Jeffrey V Rosenfeld1,2
  • Yan Tat Wong3

  • 1 Monash Institute of Medical Engineering, Monash University, Melbourne, VIC
  • 2 Department of Neurosurgery, Alfred Hospital, Melbourne, VIC
  • 3 Electrical and Computer Systems Engineering, University of Melbourne, Melbourne, VIC

Correspondence: j.rosenfeld@alfred.org.au

Competing interests:

No relevant disclosures.

  • 1. Collinger JL, Kryger MA, Barbara R, et al. Collaborative approach in the development of high-performance brain-computer interfaces for a neuroprosthetic arm: translation from animal models to human control. Clin Transl Sci 2014; 7: 52-59.
  • 2. Lewis PM, Ackland HM, Lowery AJ, Rosenfeld JV. Restoration of vision in blind individuals using bionic devices: a review with a focus on cortical visual prostheses. Brain Res 2015; 1595: 51-73.
  • 3. Lewis PM, Rosenfeld JV. Electrical stimulation of the brain and the development of cortical visual prostheses: an historical perspective. Brain Res 2016; 1630: 208-224.
  • 4. Brindley GS, Lewin WS. The sensations produced by electrical stimulation of the visual cortex. J Physiol 1968; 196: 479-493.
  • 5. Mudry A, Mills M. The early history of the cochlear implant: a retrospective. JAMA Otolaryngol Head Neck Surg 2013; 139: 446-453.
  • 6. Colletti L, Shannon R, Colletti V. Auditory brainstem implants for neurofibromatosis type 2. Curr Opin Otolaryngol Head Neck Surg 2012; 20: 353-357.
  • 7. Miranda RA, Casebeer WD, Hein AM, et al. DARPA-funded efforts in the development of novel brain-computer interface technologies. J Neurosci Methods 2015; 244: 52-67.
  • 8. Waldert S, Pistohl T, Braun C, et al. A review on directional information in neural signals for brain-machine interfaces. J Physiol Paris 2009; 103: 244-254.
  • 9. Mitzdorf U. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol Rev 1985; 65: 37-100.
  • 10. Lewis PM, Thomson RH, Rosenfeld JV, Fitzgerald PB. Brain neuromodulation techniques: a review. Neuroscientist 2016; 22: 406-421.
  • 11. Vansteensel MJ, Pels EG, Bleichner MG, et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N Engl J Med 2016; 375: 2060-2066.
  • 12. Su Y, Routhu S, Moon KS, et al. A wireless 32-channel implantable bidirectional brain machine interface. Sensors (Basel) 2016; 16: pii: E1582.
  • 13. Rajangam S, Tseng PH, Yin A, et al. Wireless cortical brain-machine interface for whole-body navigation in primates. Sci Rep 2016; 6: 22170.
  • 14. Carmena JM, Lebedev MA, Crist RE, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol 2003; 1(2): E42.
  • 15. Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006; 442: 164-171.
  • 16. Kim SP, Simeral JD, Hochberg LR, et al. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J Neural Eng 2008; 5: 455-476.
  • 17. Gilja V, Nuyujukian P, Chestek CA, et al. A high-performance neural prosthesis enabled by control algorithm design. Nat Neurosci 2012; 15: 1752-1757.
  • 18. Orsborn AL, Moorman HG, Overduin SA, et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 2014; 82: 1380-1393.
  • 19. Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 2013; 381: 557-564.
  • 20. Hochberg LR, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 2012; 485: 372-375.
  • 21. Putrino D, Wong YT, Weiss A, et al. A training platform for many-dimensional prosthetic devices using a virtual reality environment. J Neurosci Methods 2015; 244: 68-77.
  • 22. Santello M, Flanders M, Soechting JF. Postural hand synergies for tool use. J Neurosci 1998; 18: 10105-10115.
  • 23. Aflalo T, Kellis S, Klaes C, et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 2015; 348: 906-910.
  • 24. Bouton CE, Shaikhouni A, Annetta NV, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 2016; 533: 247-250.
  • 25. Capogrosso M, Milekovic T, Borton D, et al. A brain-spine interface alleviating gait deficits after spinal cord injury in primates. Nature 2016; 539: 284-288.
  • 26. Chestek CA, Gilja V, Nuyujukian P, et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J Neural Eng 2011; 8: 045005.
  • 27. Oxley TJ, Opie NL, John SE, et al. Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity. Nat Biotechnol 2016; 34: 320-327.
  • 28. Lopez-Larraz E, Trincado-Alonso F, Rajasekaran V, et al. Control of an ambulatory exoskeleton with a brain-machine interface for spinal cord injury gait rehabilitation. Front Neurosci 2016; 10: 359.
  • 29. O’Doherty JE, Lebedev MA, Ifft PJ, et al. Active tactile exploration using a brain–machine–brain interface. Nature 2011; 479: 228-231.
  • 30. Zhang F, Aravanis AM, Adamantidis A, et al. Circuit-breakers: optical technologies for probing neural signals and systems. Nat Rev Neurosci 2007; 8: 577-581.
  • 31. Clemente F, D’Alonzo M, Controzzi M, et al. Non-invasive, temporally discrete feedback of object contact and release improves grasp control of closed-loop myoelectric transradial prostheses. IEEE Trans Neural Syst Rehabil Eng 2016; 24: 1314-1322.
  • 32. Flesher SN, Collinger JL, Foldes ST, et al. Intracortical microstimulation of human somatosensory cortex. Sci Transl Med 2016; 8: 361ra141.
  • 33. Donati AR, Shokur S, Morya E, et al. Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci Rep 2016; 6: 30383.
  • 34. Lewis PM, Ayton LN, Guymer RH, et al. Advances in implantable bionic devices for blindness: a review. ANZ J Surg 2016; 86: 654-659.
  • 35. Pezaris JS, Eskandar EN. Getting signals into the brain: visual prosthetics through thalamic microstimulation. Neurosurg Focus 2009; 27: E6.
  • 36. Ponce FA, Asaad WF, Foote KD, et al. Bilateral deep brain stimulation of the fornix for Alzheimer's disease: surgical safety in the ADvance trial. J Neurosurg 2016; 125: 75-84.
  • 37. Deadwyler SA, Berger TW, Sweatt AJ, et al. Donor/recipient enhancement of memory in rat hippocampus. Front Syst Neurosci 2013; 7: 120.
  • 38. Berger TW, Hampson RE, Song D, et al. A cortical neural prosthesis for restoring and enhancing memory. J Neural Eng 2011; 8: 046017.
  • 39. Hampson RE, Gerhardt GA, Marmarelis V, et al. Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing. J Neural Eng 2012; 9: 056012.
  • 40. Strickland E. New startup aims to commercialize a brain prosthetic to improve memory. IEEE Spectrum 2016; 16 Aug. http://spectrum.ieee.org/the-human-os/biomedical/bionics/new-startup-aims-to-commercialize-a-brain-prosthetic-to-improve-memory (accessed Jan 2017).
  • 41. Schulze-Bonhage A. Brain stimulation as a neuromodulatory epilepsy therapy. Seizure 2017; 44: 169-175.
  • 42. Freestone DR, Karoly PJ, Peterson AD, et al. Seizure prediction: science fiction or soon to become reality? Curr Neurol Neurosci Rep 2015; 15: 73.
  • 43. Ludvig N, Tang HM, Baptiste SL, et al. Long-term behavioral, electrophysiological, and neurochemical monitoring of the safety of an experimental antiepileptic implant, the muscimol-delivering Subdural Pharmacotherapy Device in monkeys. J Neurosurg 2012; 117: 162-175.
  • 44. Ludvig N, Medveczky G, Rizzolo R, et al. An implantable triple-function device for local drug delivery, cerebrospinal fluid removal and EEG recording in the cranial subdural/subarachnoid space of primates. J Neurosci Methods 2012; 203: 275-283.
  • 45. Rowland NC, Sammartino F, Lozano AM. Advances in surgery for movement disorders. Mov Disord 2017; 32: 5-10.
  • 46. Kuhn AA, Volkmann J. Innovations in deep brain stimulation methodology. Mov Disord 2017; 32: 11-19.
  • 47. Hughes MA. Engineering brain-computer interfaces: past, present and future. J Neurosurg Sci 2014; 58: 117-123.
  • 48. Schouenborg J. Biocompatible multichannel electrodes for long-term neurophysiological studies and clinical therapy–novel concepts and design. Prog Brain Res 2011; 194: 61-70.
  • 49. Hong G, Fu TM, Zhou T, et al. Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity. Nano Lett 2015; 15: 6979-6984.
  • 50. Kim TI, McCall JG, Jung YH, et al. Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science 2013; 340: 211-216.
  • 51. Guduru R, Liang P, Hong J, et al. Magnetoelectric 'spin' on stimulating the brain. Nanomedicine (Lond) 2015; 10: 2051-2061.
  • 52. Mandavilli A. Actions speak louder than images. Nature 2006; 444: 664-665.
  • 53. Ovadia D, Bottini G. Neuroethical implications of deep brain stimulation in degenerative disorders. Curr Opin Neurol 2015; 28: 598-603.
  • 54. Wartolowska K, Judge A, Hopewell S, et al. Use of placebo controls in the evaluation of surgery: systematic review. BMJ 2014; 348: g3253.
  • 55. Katsnelson A. Experimental therapies for Parkinson's disease: why fake it? Nature 2011; 476: 142-144.
  • 56. Swift T, Huxtable R. The ethics of sham surgery in Parkinson's disease: back to the future? Bioethics 2013; 27: 175-185.
  • 57. Harris I. Surgery, the ultimate placebo. 1st ed. Sydney: UNSW Press NewSouth, 2016.
  • 58. Freed CR, Greene PE, Breeze RE, et al. Transplantation of embryonic dopamine neurons for severe Parkinson's disease. N Engl J Med 2001; 344: 710-719.
  • 59. LeWitt PA, Rezai AR, Leehey MA, et al. AAV2-GAD gene therapy for advanced Parkinson's disease: a double-blind, sham-surgery controlled, randomised trial. Lancet Neurol 2011; 10: 309-319.
  • 60. Gross RE, Watts RL, Hauser RA, et al. Intrastriatal transplantation of microcarrier-bound human retinal pigment epithelial cells versus sham surgery in patients with advanced Parkinson's disease: a double-blind, randomised, controlled trial. Lancet Neurol 2011; 10: 509-519.
  • 61. Lane FJ, Huyck M, Troyk P, et al. Responses of potential users to the intracortical visual prosthesis: final themes from the analysis of focus group data. Disabil Rehabil Assist Technol 2012; 7: 304-313.

Author

remove_circle_outline Delete Author
add_circle_outline Add Author

Comment
Do you have any competing interests to declare? *

I/we agree to assign copyright to the Medical Journal of Australia and agree to the Conditions of publication *
I/we agree to the Terms of use of the Medical Journal of Australia *
Email me when people comment on this article

Responses are now closed for this article.