Skip to main content

Neurosurgical Considerations for the Brain Computer Interface

  • Living reference work entry
  • First Online:
Handbook of Neuroengineering

Abstract

Brain Computer Interface (BCI) devices are being developed to restore or improve the function of people suffering with neurological disabilities such as limb paralysis, sensory loss, blindness, deafness, movement disorders, memory loss, loss of speech and epilepsy. BCI technology is advancing rapidly but major challenges remain, such as biocompatibility, developing micro-electrodes which function effectively for many years, maintenance of device hermeticity, improved wireless functionality and the ability to simultaneously stimulate and record from a neural network of thousands or millions of neurons. Non-invasive devices are attractive but are far less capable compared with the spatial or functional resolution of implanted devices.

Engineers need to collaborate with neurosurgeons to develop devices that significantly benefit the patient, are practical to implant with low and acceptable surgical risks, have a record of long-term safety, and can be safely explanted. Neurosurgeons should be involved in the preclinical (animal) testing of prototype devices and they need to understand the technical capability of the BCI and its experimental results to be able to discuss the BCI with the potential recipients and their families so that they can give informed consent.

This chapter describes the neurosurgery/engineering collaboration necessary to successfully develop test and commercialize BCIs and also presents the full range of BCI applications. The ethical considerations concerning the development and application of BCIs will also be presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Abbreviations

AIMD:

Active Implantable Medical Device

ASIC:

Application Specific Integrated Circuit

BCI:

Brain Computer Interfaces

CE:

Conformité Européenne

COI:

Conflict of Interest

CSF:

Cerebral spinal fluid

CT:

Computed Tomography

DBS:

Deep brain stimulation

ECoG:

Electrocorticography

EEG:

Electroencephalogram

FDA:

Food and Drug Administration

HREC:

Human Research Ethics Committee

LGN:

Lateral geniculate nucleus

MRI:

Magnetic Resonance Imaging

NHMRC:

National Health and Medical Research Council (of Australia)

NHP:

Non-human primates

NMPA:

National Medical Products Administration

OCD:

Obsessive compulsive disorder

PPC:

Posterior Parietal Cortex

SSEPs:

Somatosensory evoked potentials

TGA:

Therapeutic Goods Administration (of Australia)

References

  1. Rosenfeld, J.V., Wong, Y.T.: Neurobionics and the brain–computer interface: current applications and future horizons. Med. J. Aust. 206(8), 363–368 (2017)

    Article  Google Scholar 

  2. Marshall, S.A., Riechers II, R.G.: Diagnosis and management of moderate and severe traumatic brain injury sustained in combat. Mil. Med. 177(suppl_8), 76–85 (2012)

    Article  Google Scholar 

  3. Vidal, J.: Real-time detection of brain events in EEG. IEEE Proc. 65(5), 633–644 (1977)

    Article  Google Scholar 

  4. Lewis, P.M., Rosenfeld, J.V.: Electrical stimulation of the brain and the development of cortical visual prostheses: an historical perspective. Brain Res. 1630, 208–224 (2016)

    Article  Google Scholar 

  5. Lewis, P.M., et al.: Advances in implantable bionic devices for blindness: a review. ANZ J. Surg. 86(9), 654–659 (2016)

    Article  Google Scholar 

  6. Hochberg, L.R., et al.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 442(7099), 164–171 (2006)

    Article  Google Scholar 

  7. Andersen, R.: The intention machine. A new generation of brain-machine interface can deduce what a person wants. Sci. Am., 18–25 (2019)

    Google Scholar 

  8. Aflalo, T., et al.: Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science. 348(6237), 906–910 (2015)

    Article  Google Scholar 

  9. Deadwyler, S.A., et al.: Donor/recipient enhancement of memory in rat hippocampus. Front. Syst. Neurosci. 7, 120 (2013)

    Article  Google Scholar 

  10. Deadwyler, S.A., et al.: A cognitive prosthesis for memory facilitation by closed-loop functional ensemble stimulation of hippocampal neurons in primate brain. Exp. Neurol. 287(Pt 4), 452–460 (2017)

    Article  Google Scholar 

  11. Cogan, S.F.: Neural stimulation and recording electrodes. Annu. Rev. Biomed. Eng. 10(1), 275–309 (2008)

    Article  Google Scholar 

  12. Patil, A.C., Thakor, N.V.: Implantable neurotechnologies: a review of micro- and nanoelectrodes for neural recording. Med. Biol. Eng. Comput. 54(1), 23–44 (2016)

    Article  Google Scholar 

  13. Wong, Y.T., et al.: Utilizing movement synergies to improve decoding performance for a brain machine interface. In: Conference proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013. (2013), pp. 289–292

    Google Scholar 

  14. Wong, Y.T., et al.: Decoding arm and hand movements across layers of the macaque frontal cortices. In: Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012. (2012), pp. 1757–1760

    Google Scholar 

  15. Rosenfeld, J.V., et al.: Tissue response to a chronically implantable wireless, intracortical visual prosthesis (Gennaris array). J. Neural Eng. 17, 046001 (2020)

    Google Scholar 

  16. Dobelle, W.H., Mladejovsky, M.G., Girvin, J.P.: Artificial vision for the blind: electrical stimulation of visual cortex offers hope for a functional prosthesis. Science. 183(4123), 440 (1974)

    Article  Google Scholar 

  17. Kinoshita, M., et al.: Electric stimulation on human cortex suppresses fast cortical activity and epileptic spikes. Epilepsia. 45(7), 787–791 (2004)

    Article  Google Scholar 

  18. Normann, R.A., et al.: A neural interface for a cortical vision prosthesis. Vis. Res. 39(15), 2577–2587 (1999)

    Article  Google Scholar 

  19. Flesher, S.N., et al.: Intracortical microstimulation of human somatosensory cortex. Sci. Transl. Med. 8(361), 361ra141 (2016)

    Article  Google Scholar 

  20. Pudenz, R.H.: Neural stimulation: clinical and laboratory experiences. Surg. Neurol. 39(3), 235–242 (1993)

    Article  Google Scholar 

  21. Wong, Y.T., et al.: Retinal neurostimulator for a multifocal vision prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 425–434 (2007)

    Article  Google Scholar 

  22. Dommel, N.B., et al.: A CMOS retinal neurostimulator capable of focussed, simultaneous stimulation. J. Neural Eng. 6, 035006 (2009) (1741–2552 (Electronic))

    Article  Google Scholar 

  23. Osorio, I., et al.: Performance reassessment of a real-time seizure-detection algorithm on long ECoG series. Epilepsia. 43(12), 1522–1535 (2002)

    Article  Google Scholar 

  24. Slutzky, M.W., et al.: Optimal spacing of surface electrode arrays for brain-machine interface applications. J. Neural Eng. 7(2), 26004–26004 (2010)

    Article  Google Scholar 

  25. Rouse, A.G., et al.: Spatial co-adaptation of cortical control columns in a micro-ECoG brain–computer interface. J. Neural Eng. 13(5), 056018 (2016)

    Article  Google Scholar 

  26. Kim, D.-H., et al.: Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics. Nat. Mater. 9, 511 (2010)

    Article  Google Scholar 

  27. Yu, K.J., et al.: Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex. Nat. Mater. 15, 782 (2016)

    Article  Google Scholar 

  28. Yanagisawa, T., et al.: Neural decoding using gyral and intrasulcal electrocorticograms. NeuroImage. 45(4), 1099–1106 (2009)

    Article  Google Scholar 

  29. Li, J., et al.: Conductively coupled flexible silicon electronic systems for chronic neural electrophysiology. Proc. Natl. Acad. Sci. 115(41), E9542 (2018)

    Article  Google Scholar 

  30. Viventi, J., et al.: Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nat. Neurosci. 14(12), 1599–1605 (2011)

    Article  Google Scholar 

  31. Degenhart, A.D., et al.: Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate. J. Neural Eng. 13(4), 046019–046019 (2016)

    Article  Google Scholar 

  32. Markowitz, D.A., et al.: Optimizing the decoding of movement goals from local field potentials in macaque cortex. J. Neurosci. 31(50), 18412 (2011)

    Article  Google Scholar 

  33. Davis, T.S., et al.: Spatial and temporal characteristics of V1 microstimulation during chronic implantation of a microelectrode array in a behaving macaque. J. Neural Eng. 9(6), 065003 (2012)

    Article  Google Scholar 

  34. Maynard, E.M., Nordhausen, C.T., Normann, R.A.: The Utah Intracortical electrode Array: a recording structure for potential brain-computer interfaces. Electroencephalogr. Clin. Neurophysiol. 102(3), 228–239 (1997)

    Article  Google Scholar 

  35. Hsieh, H.-L., et al.: Multiscale modeling and decoding algorithms for spike-field activity. J. Neural Eng. 16(1), 016018 (2018)

    Article  Google Scholar 

  36. Collinger, J.L., et al.: High-performance neuroprosthetic control by an individual with tetraplegia. Lancet (London, England). 381(9866), 557–564 (2013)

    Article  Google Scholar 

  37. Kipke, D.R., et al.: Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 151–155 (2003)

    Article  Google Scholar 

  38. Nicolelis, M.A.L., et al.: Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl. Acad. Sci. 100(19), 11041 (2003)

    Article  Google Scholar 

  39. Jun, J.J., et al.: Fully integrated silicon probes for high-density recording of neural activity. Nature. 551, 232 (2017)

    Article  Google Scholar 

  40. Barz, F., et al.: Versatile, modular 3D microelectrode arrays for neuronal ensemble recordings: from design to fabrication, assembly, and functional validation in non-human primates. J. Neural Eng. 14(3), 036010 (2017)

    Article  Google Scholar 

  41. Douglas, R.J., Martin, K.A.C.: Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27(1), 419–451 (2004)

    Article  Google Scholar 

  42. DeYoe, E.A., Lewine, J.D., Doty, R.W.: Laminar variation in threshold for detection of electrical excitation of striate cortex by macaques. J. Neurophysiol. 94(5), 3443–3450 (2005)

    Article  Google Scholar 

  43. Firszt, J.B., et al.: Current steering creates additional pitch percepts in adult Cochlear implant recipients. Otol. Neurotol. 28(5) (2007)

    Google Scholar 

  44. Srinivasan, A.G., Landsberger, D.M., Shannon, R.V.: Current focusing sharpens local peaks of excitation in cochlear implant stimulation. Hear. Res. 270(1), 89–100 (2010)

    Article  Google Scholar 

  45. Polikov, V.S., Tresco, P.A., Reichert, W.M.: Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods. 148(1), 1–18 (2005)

    Article  Google Scholar 

  46. Jepson, L.H., et al.: Spatially patterned electrical stimulation to enhance resolution of retinal prostheses. J. Neurosci. 34(14), 4871 (2014)

    Article  Google Scholar 

  47. Butson, C.R., McIntyre, C.C.: Current steering to control the volume of tissue activated during deep brain stimulation. Brain Stimul. 1(1), 7–15 (2008)

    Article  Google Scholar 

  48. Timmermann, L., et al.: Multiple-source current steering in subthalamic nucleus deep brain stimulation for Parkinson’s disease (the VANTAGE study): a non-randomised, prospective, multicentre, open-label study. Lancet Neurol. 14(7), 693–701 (2015)

    Article  Google Scholar 

  49. Rousche, P.J., Normann, R.A.: A method for pneumatically inserting an array of penetrating electrodes into cortical tissue. Ann. Biomed. Eng. 20(4), 413–422 (1992)

    Article  Google Scholar 

  50. Liu, J., et al.: Syringe-injectable electronics. Nat. Nanotechnol. 10, 629 (2015)

    Article  Google Scholar 

  51. Barrese, J.C., Aceros, J., Donoghue, J.P.: Scanning electron microscopy of chronically implanted intracortical microelectrode arrays in non-human primates. J. Neural Eng. 13(2), 026003–026003 (2016)

    Article  Google Scholar 

  52. Kozai, T.D.Y., et al.: Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces. Nat. Mater. 11(12), 1065–1073 (2012)

    Article  Google Scholar 

  53. Kozai, T.D.Y., Kipke, D.R.: Insertion shuttle with carboxyl terminated self-assembled monolayer coatings for implanting flexible polymer neural probes in the brain. J. Neurosci. Methods. 184(2), 199–205 (2009)

    Article  Google Scholar 

  54. Apollo, N.V., et al.: Soft, flexible freestanding neural stimulation and recording electrodes fabricated from reduced graphene oxide. Adv. Funct. Mater. 25(23), 3551–3559 (2015)

    Article  Google Scholar 

  55. Keefer, E.W., et al.: Carbon nanotube coating improves neuronal recordings. Nat. Nanotechnol. 3, 434 (2008)

    Article  Google Scholar 

  56. Barrese, J.C., et al.: Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates. J. Neural Eng. 10(6), 066014 (2013)

    Article  Google Scholar 

  57. Mitz, A.R., et al.: High channel count single-unit recordings from nonhuman primate frontal cortex. J. Neurosci. Methods. 289, 39–47 (2017)

    Article  Google Scholar 

  58. Zhou, A., et al.: A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates. Nature Biomed. Eng. 3(1), 15–26 (2019)

    Article  Google Scholar 

  59. Wong, Y.T., et al.: CMOS stimulating chips capable of wirelessly driving 473 electrodes for a cortical vision prosthesis. J. Neural Eng. 16(2), 026025 (2019)

    Article  Google Scholar 

  60. Oxley, T.J., et al.: Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity. Nat. Biotechnol. 34, 320 (2016)

    Article  Google Scholar 

  61. Opie, N.L., et al.: Focal stimulation of the sheep motor cortex with a chronically implanted minimally invasive electrode array mounted on an endovascular stent. Nature Biomed. Eng. 2(12), 907–914 (2018)

    Article  Google Scholar 

  62. John, S.E., et al.: Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable. Sci. Rep. 8(1), 8427 (2018)

    Article  Google Scholar 

  63. Gerboni, G., et al.: Visual evoked potentials determine chronic signal quality in a stent-electrode endovascular neural interface. Biomed. Phys. Eng. Express. 4(5), 055018 (2018)

    Article  Google Scholar 

  64. He, B.D., et al.: Signal quality of endovascular electroencephalography. J. Neural Eng. 13(1), 016016 (2016)

    Article  Google Scholar 

  65. Gerboni, G., et al.: Cortical brain stimulation with endovascular electrodes. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2018

    Google Scholar 

  66. Wang, C., et al.: Characteristics of electrode impedance and stimulation efficacy of a chronic cortical implant using novel annulus electrodes in rat motor cortex. J. Neural Eng. 10(4), 046010 (2013) %@ 1741-2552

    Article  Google Scholar 

  67. Piallat, B., et al.: Monophasic but not biphasic pulses induce brain tissue damage during monopolar high-frequency deep brain stimulation. Neurosurgery. 64(1), 156–163 (2009)

    Article  Google Scholar 

  68. Brunton, E., Lowery, A.J., Rajan, R.: A comparison of microelectrodes for a visual cortical prosthesis using finite element analysis. Front. Neuroeng. 5, 23–23 (2012)

    Article  Google Scholar 

  69. Martin, S., Duncan, E.: Sterilisation considerations for implantable sensor systems. Implant. Sensor Syst. Med. Appl., 253–278 (2013)

    Google Scholar 

  70. Schuettler, M., Stieglitz, T.: Microassembly and micropackaging of implantable systems. Implant. Sensor Syst. Med. Appl., 108–149 (2013)

    Google Scholar 

  71. Gokhale, S., et al.: Levetiracetam seizure prophylaxis in craniotomy patients at high risk for postoperative seizures. Asian J. Neurosurg. 8(4), 169–169 (2013)

    Article  Google Scholar 

  72. Abode-Iyamah, K.O., et al.: Deep brain stimulation hardware-related infections: 10-year experience at a single institution. J. Neurosurg., 1–10 (2018)

    Google Scholar 

  73. Standardization, I.O.f.,: ISO 14971 – Application of risk management to medical devices. 2007

    Google Scholar 

  74. Cogan, S.F., et al.: Tissue damage thresholds during therapeutic electrical stimulation. J. Neural Eng. 13(2), 021001–021001 (2016)

    Article  Google Scholar 

  75. National Health and Medical Research Council, National Statement on Ethical Conduct in Human Research. (2018)

    Google Scholar 

  76. Lewis, P.M., et al.: Restoration of vision in blind individuals using bionic devices: a review with a focus on cortical visual prostheses. Brain Res. 1595, 51–73 (2015)

    Article  Google Scholar 

  77. Pezaris, J.S., Reid, R.C.: Demonstration of artificial visual percepts generated through thalamic microstimulation. Proc. Natl. Acad. Sci. U. S. A. 104(18), 7670–7675 (2007)

    Article  Google Scholar 

  78. Veraart, C., et al.: Pattern recognition with the optic nerve visual prosthesis. Artif. Organs. 27(11), 996–1004 (2003)

    Article  Google Scholar 

  79. Luo, Y.H., da Cruz, L.: The Argus((R)) II retinal prosthesis system. Prog. Retin. Eye Res. 50, 89–107 (2016)

    Article  Google Scholar 

  80. Deep, N.L., et al.: Cochlear implantation: an overview. J. Neurol. Surg. Part B, Skull Base. 80(2), 169–177 (2019)

    Article  Google Scholar 

  81. Peng, K.A., et al.: Cochlear implantation and auditory brainstem implantation in neurofibromatosis type 2. Laryngoscope. 128(9), 2163–2169 (2018)

    Article  Google Scholar 

  82. House, W.F., Hitselberger, W.E.: Twenty-year report of the first auditory brain stem nucleus implant. Ann. Otol. Rhinol. Laryngol. 110(2), 103–104 (2001)

    Article  Google Scholar 

  83. Weintraub, K.: Aroma therapy. Sci. Am., 320(4), 8–10 (2019)

    Google Scholar 

  84. Holbrook, E.H., et al.: Induction of smell through transethmoid electrical stimulation of the olfactory bulb. Int. Forum. Allergy Rhinol. 9(2), 158–164 (2019)

    Article  Google Scholar 

  85. Kim, S., et al.: Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc. Natl. Acad. Sci. U. S. A. 112(49), 15202–15207 (2015)

    Article  Google Scholar 

  86. Hiremath, S.V., et al.: Human perception of electrical stimulation on the surface of somatosensory cortex. PLoS One. 12(5) (2017)

    Google Scholar 

  87. Charles, P.D., et al.: Predictors of effective bilateral subthalamic nucleus stimulation for PD. Neurology. 59(6), 932–934 (2002)

    Article  Google Scholar 

  88. Lozano, A.M., et al.: Deep brain stimulation: current challenges and future directions. Nat. Rev. Neurol. 15(3), 148–160 (2019)

    Article  Google Scholar 

  89. Akbari, H., et al.: Towards reconstructing intelligible speech from the human auditory cortex. Sci. Rep. 9(1), 874 (2019)

    Article  Google Scholar 

  90. Anumanchipalli, G.K., Chartier, J., Chang, E.F.: Speech synthesis from neural decoding of spoken sentences. Nature. 568(7753), 493–498 (2019)

    Article  Google Scholar 

  91. Hochberg, L.R., et al.: Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 485(7398), 372–375 (2012)

    Article  Google Scholar 

  92. Cook, M.J., et al.: Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 12(6), 563–571 (2013)

    Article  Google Scholar 

  93. Ben-Menachem, E.: Evaluation of refractory epilepsy treated with vagus never stimulation for up to 5 years. Neurology. 52, 1117–1118 (1999)

    Article  Google Scholar 

  94. Heck, C.N., et al.: Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS system pivotal trial. Epilepsia. 55(3), 432–441 (2014)

    Article  Google Scholar 

  95. Gummadavelli, A., et al.: Expanding brain-computer interfaces for controlling epilepsy networks: novel thalamic responsive neurostimulation in refractory epilepsy. Front. Neurosci. 12, 474 (2018)

    Article  Google Scholar 

  96. Stanslaski, S., et al.: Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 20(4), 410–421 (2012)

    Article  Google Scholar 

  97. Rosin, B., et al.: Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron. 72(2), 370–384 (2011)

    Article  Google Scholar 

  98. Quinn, E.J., et al.: Beta oscillations in freely moving Parkinson’s subjects are attenuated during deep brain stimulation. Mov. Disord. 30(13), 1750–1758 (2015)

    Article  Google Scholar 

  99. Neumann, W.J., et al.: Toward electrophysiology-based intelligent adaptive deep brain stimulation for movement disorders. Neurotherapeutics. 16(1), 105–118 (2019)

    Article  Google Scholar 

  100. Ghasemi, P., Sahraee, T., Mohammadi, A.: Closed- and open-loop deep brain stimulation: methods, challenges, current and future aspects. J. Biomed. Phys. Eng. 8(2), 209–216 (2018)

    Article  Google Scholar 

  101. Priori, A., et al.: Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp. Neurol. 245, 77–86 (2013)

    Article  Google Scholar 

  102. Berger, T.W., et al.: A cortical neural prosthesis for restoring and enhancing memory. J. Neural Eng. 8(4), 046017 (2011)

    Article  Google Scholar 

  103. Hampson, R.E., et al.: Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing. J. Neural Eng. 9(5), 056012 (2012)

    Article  Google Scholar 

  104. Roskies, A.L.: Agency and intervention. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 370(1677), 20140215 (2015)

    Article  Google Scholar 

  105. Rosenfeld, J.V., Broekmann, M.: Brain machine interface technology in neurosurgery. In: Ethics in neurosurgical practice. Cambridge University Press, Cambridge, UK (2019)

    Google Scholar 

  106. Vincent, C.: Auditory brainstem implants: how do they work? Anat. Rec. (Hoboken). 295(11), 1981–1986 (2012)

    Article  Google Scholar 

  107. Otto, S.R., et al.: Multichannel auditory brainstem implant: update on performance in 61 patients. J. Neurosurg. 96(6), 1063–1071 (2002)

    Article  Google Scholar 

  108. Varma, R., et al.: Visual impairment and blindness in adults in the United States: demographic and geographic variations from 2015 to 2050. JAMA Ophthalmol. 134(7), 802–809 (2016)

    Article  Google Scholar 

  109. Maynard, E.M.: Visual prostheses. Annu. Rev. Biomed. Eng. 3, 145–168 (2001)

    Article  Google Scholar 

  110. Margalit, E., et al.: Retinal prosthesis for the blind. Surv. Ophthalmol. 47(4), 335–356 (2002)

    Article  Google Scholar 

  111. Niketeghad, S., Pouratian, N.: Brain machine interfaces for vision restoration: the current state of cortical visual prosthetics. Neurotherapeutics. 16(1), 134–143 (2019)

    Article  Google Scholar 

  112. Edwards, T.L., et al.: Assessment of the electronic retinal implant Alpha AMS in restoring vision to blind patients with end-stage retinitis pigmentosa. Ophthalmology. 125(3), 432–443 (2018)

    Article  Google Scholar 

  113. Stingl, K., et al.: What can blind patients see in daily life with the subretinal Alpha IMS implant? Current overview from the clinical trial in Tubingen. Ophthalmologe. 109(2), 136–141 (2012)

    Article  Google Scholar 

  114. Panetsos, F., et al.: Consistent Phosphenes generated by electrical microstimulation of the visual thalamus. An experimental approach for thalamic visual neuroprostheses. Front. Neurosci. 5(84) (2011)

    Google Scholar 

  115. Najarpour Foroushani, A., Pack, C.C., Sawan, M.: Cortical visual prostheses: from microstimulation to functional percept. J. Neural Eng. 15(2), 021005 (2018)

    Article  Google Scholar 

  116. Bosking, W.H., Beauchamp, M.S., Yoshor, D.: Electrical stimulation of visual cortex: relevance for the development of visual cortical prosthetics. Annu. Rev. Vis. Sci. 3, 141–166 (2017)

    Article  Google Scholar 

  117. Beauchamp, M.S., et al.: Dynamic stimulation of visual cortex produces form vision in sighted and blind humans. Cell. 181(4), 774–783.e5 (2020)

    Article  Google Scholar 

  118. Ayton, L.N., et al.: Harmonization of outcomes and vision endpoints in vision restoration trials: recommendations from the international HOVER taskforce. Transl. Vis. Sci. Technol. 9(8), 25–25 (2020)

    Article  Google Scholar 

  119. Yoo, P.E., et al.: Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals. Hum. Brain Mapp. 39(6), 2635–2650 (2018)

    Article  Google Scholar 

  120. Wang, W., et al.: An electrocorticographic brain interface in an individual with tetraplegia. PLoS One. 8(2), e55344 (2013)

    Article  Google Scholar 

  121. Yanagisawa, T., et al.: Electrocorticographic control of a prosthetic arm in paralyzed patients. Ann. Neurol. 71(3), 353–361 (2012)

    Article  Google Scholar 

  122. Even-Chen, N., et al.: Power-saving design opportunities for wireless intracortical brain–computer interfaces. Nature Biomed. Eng. 4, 984–996 (2020)

    Google Scholar 

  123. Bouton, C.E., et al.: Restoring cortical control of functional movement in a human with quadriplegia. Nature. 533(7602), 247–250 (2016)

    Article  Google Scholar 

  124. Vansteensel, M.J., Bleichner, M.G., Branco, M.P., Denison, T., Freudenberg, Z.V., Gosselar, P., Leinders, S., Ottens, T.H., Van Den Boom, M.A., Van Rijen, P.C., Aarnouste, E.J., Ramsey, N.F.: Fully implanted brain-computer interface in a locked-in patients with ALS. N. Engl. J. Med. 375, 2060–2066 (2016)

    Google Scholar 

  125. Oddo, C.M., et al.: Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans. elife. 5, e09148 (2016)

    Article  Google Scholar 

  126. Martin, S., et al.: Decoding inner speech using electrocorticography: progress and challenges toward a speech prosthesis. Front. Neurosci. 12, 422 (2018)

    Article  Google Scholar 

  127. Weiss, J.M., et al.: Artifact-free recordings in human bidirectional brain–computer interfaces. J. Neural Eng. 16(1), 016002 (2018)

    Article  Google Scholar 

  128. Dexter, D.T., Jenner, P.: Parkinson disease: from pathology to molecular disease mechanisms. Free Radic. Biol. Med. 62, 132–144 (2013)

    Article  Google Scholar 

  129. Kwan, P., Brodie, M.J.: Early identification of refractory epilepsy. N. Engl. J. Med. 342(5), 314–319 (2000)

    Article  Google Scholar 

  130. Freestone, D.R., et al.: Seizure prediction: science fiction or soon to become reality? Curr. Neurol. Neurosci. Rep. 15(11), 73 (2015)

    Article  Google Scholar 

  131. Ludvig, N., 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. 203(2), 275–283 (2012)

    Article  Google Scholar 

  132. Ludvig, N., 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. 117(1), 162–175 (2012)

    Article  Google Scholar 

  133. Strickland, E.: New startup aims to commercialise a brain prosthetic to improve memory. IEEE Spectr. (2016)

    Google Scholar 

  134. Ponce, F.A., et al.: Bilateral deep brain stimulation of the fornix for Alzheimer’s disease: surgical safety in the advance trial. J. Neurosurg. 125(1), 75–84 (2016)

    Article  Google Scholar 

  135. Hong, G., et al.: Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity. Nano Lett. 15(10), 6979–6984 (2015)

    Article  Google Scholar 

  136. Kim, T.I., et al.: Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science. 340(6129), 211–216 (2013)

    Article  Google Scholar 

  137. Lee, S.W., et al.: Implantable microcoils for intracortical magnetic stimulation. Sci. Adv. 2(12), e1600889 (2016)

    Article  Google Scholar 

  138. Martins, N.R.B., et al.: Human brain/cloud Interface. Front. Neurosci. 13, 112 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeffrey V. Rosenfeld .

Editor information

Editors and Affiliations

Section Editor information

Appendix A

Appendix A

1.1 Ethics Application for the Pilot Study of a Bionic Vision Device

figure a

1.2 General Information and Investigator Declaration

Principal Investigator:

I, [Principal Investigator], agree to conduct this clinical trial according to the declaration of Helsinki (2000), International Conference on Harmonisation Good Clinical Practice (ICH-GCP), and to abide by the protocol [Protocol ID]. [The Institution responsible] will conduct the study in strict compliance with this protocol.

On behalf of [The Institution],

[signature]

[Principal Investigator]

Principal Investigator

Associate investigators

[Associate Investigator]

[Associate Investigator]

[Associate Investigator]

A.3 Notes on Human Ethics Applicaitons

  1. (a)

    This is an example of what information is required by a hospital or institutional Human Research Ethics Committee to conduct a first-in-human clinical study of a new neural prosthesis such as a bionic vision device in the brain.

  2. (b)

    The investigators also must sign a Declaration of Compliance with accepted ethical practices.

  3. (c)

    The investigators must also produce a Plain Language Statement for the trial participants so that they can reflect in their own time what the study involves and raise any further questions of the investigators. It is written in language that the participants would easily be able to understand. Great care should be taken in preparing this document. The ethics committee will ensure that it meets the standards required to achieve informed patient consent. The potential risks must be included and explained very clearly.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Rosenfeld, J.V., Wong, Y., Szlawski, J., Gutman, M. (2022). Neurosurgical Considerations for the Brain Computer Interface. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2848-4_38-1

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2848-4_38-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2848-4

  • Online ISBN: 978-981-15-2848-4

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

Publish with us

Policies and ethics