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.
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
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)
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)
Vidal, J.: Real-time detection of brain events in EEG. IEEE Proc. 65(5), 633–644 (1977)
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)
Lewis, P.M., et al.: Advances in implantable bionic devices for blindness: a review. ANZ J. Surg. 86(9), 654–659 (2016)
Hochberg, L.R., et al.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 442(7099), 164–171 (2006)
Andersen, R.: The intention machine. A new generation of brain-machine interface can deduce what a person wants. Sci. Am., 18–25 (2019)
Aflalo, T., et al.: Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science. 348(6237), 906–910 (2015)
Deadwyler, S.A., et al.: Donor/recipient enhancement of memory in rat hippocampus. Front. Syst. Neurosci. 7, 120 (2013)
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)
Cogan, S.F.: Neural stimulation and recording electrodes. Annu. Rev. Biomed. Eng. 10(1), 275–309 (2008)
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)
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
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
Rosenfeld, J.V., et al.: Tissue response to a chronically implantable wireless, intracortical visual prosthesis (Gennaris array). J. Neural Eng. 17, 046001 (2020)
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)
Kinoshita, M., et al.: Electric stimulation on human cortex suppresses fast cortical activity and epileptic spikes. Epilepsia. 45(7), 787–791 (2004)
Normann, R.A., et al.: A neural interface for a cortical vision prosthesis. Vis. Res. 39(15), 2577–2587 (1999)
Flesher, S.N., et al.: Intracortical microstimulation of human somatosensory cortex. Sci. Transl. Med. 8(361), 361ra141 (2016)
Pudenz, R.H.: Neural stimulation: clinical and laboratory experiences. Surg. Neurol. 39(3), 235–242 (1993)
Wong, Y.T., et al.: Retinal neurostimulator for a multifocal vision prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 425–434 (2007)
Dommel, N.B., et al.: A CMOS retinal neurostimulator capable of focussed, simultaneous stimulation. J. Neural Eng. 6, 035006 (2009) (1741–2552 (Electronic))
Osorio, I., et al.: Performance reassessment of a real-time seizure-detection algorithm on long ECoG series. Epilepsia. 43(12), 1522–1535 (2002)
Slutzky, M.W., et al.: Optimal spacing of surface electrode arrays for brain-machine interface applications. J. Neural Eng. 7(2), 26004–26004 (2010)
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)
Kim, D.-H., et al.: Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics. Nat. Mater. 9, 511 (2010)
Yu, K.J., et al.: Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex. Nat. Mater. 15, 782 (2016)
Yanagisawa, T., et al.: Neural decoding using gyral and intrasulcal electrocorticograms. NeuroImage. 45(4), 1099–1106 (2009)
Li, J., et al.: Conductively coupled flexible silicon electronic systems for chronic neural electrophysiology. Proc. Natl. Acad. Sci. 115(41), E9542 (2018)
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)
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)
Markowitz, D.A., et al.: Optimizing the decoding of movement goals from local field potentials in macaque cortex. J. Neurosci. 31(50), 18412 (2011)
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)
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)
Hsieh, H.-L., et al.: Multiscale modeling and decoding algorithms for spike-field activity. J. Neural Eng. 16(1), 016018 (2018)
Collinger, J.L., et al.: High-performance neuroprosthetic control by an individual with tetraplegia. Lancet (London, England). 381(9866), 557–564 (2013)
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)
Nicolelis, M.A.L., et al.: Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl. Acad. Sci. 100(19), 11041 (2003)
Jun, J.J., et al.: Fully integrated silicon probes for high-density recording of neural activity. Nature. 551, 232 (2017)
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)
Douglas, R.J., Martin, K.A.C.: Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27(1), 419–451 (2004)
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)
Firszt, J.B., et al.: Current steering creates additional pitch percepts in adult Cochlear implant recipients. Otol. Neurotol. 28(5) (2007)
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)
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)
Jepson, L.H., et al.: Spatially patterned electrical stimulation to enhance resolution of retinal prostheses. J. Neurosci. 34(14), 4871 (2014)
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)
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)
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)
Liu, J., et al.: Syringe-injectable electronics. Nat. Nanotechnol. 10, 629 (2015)
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)
Kozai, T.D.Y., et al.: Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces. Nat. Mater. 11(12), 1065–1073 (2012)
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)
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)
Keefer, E.W., et al.: Carbon nanotube coating improves neuronal recordings. Nat. Nanotechnol. 3, 434 (2008)
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)
Mitz, A.R., et al.: High channel count single-unit recordings from nonhuman primate frontal cortex. J. Neurosci. Methods. 289, 39–47 (2017)
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)
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)
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)
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)
John, S.E., et al.: Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable. Sci. Rep. 8(1), 8427 (2018)
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)
He, B.D., et al.: Signal quality of endovascular electroencephalography. J. Neural Eng. 13(1), 016016 (2016)
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
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
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)
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)
Martin, S., Duncan, E.: Sterilisation considerations for implantable sensor systems. Implant. Sensor Syst. Med. Appl., 253–278 (2013)
Schuettler, M., Stieglitz, T.: Microassembly and micropackaging of implantable systems. Implant. Sensor Syst. Med. Appl., 108–149 (2013)
Gokhale, S., et al.: Levetiracetam seizure prophylaxis in craniotomy patients at high risk for postoperative seizures. Asian J. Neurosurg. 8(4), 169–169 (2013)
Abode-Iyamah, K.O., et al.: Deep brain stimulation hardware-related infections: 10-year experience at a single institution. J. Neurosurg., 1–10 (2018)
Standardization, I.O.f.,: ISO 14971 – Application of risk management to medical devices. 2007
Cogan, S.F., et al.: Tissue damage thresholds during therapeutic electrical stimulation. J. Neural Eng. 13(2), 021001–021001 (2016)
National Health and Medical Research Council, National Statement on Ethical Conduct in Human Research. (2018)
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)
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)
Veraart, C., et al.: Pattern recognition with the optic nerve visual prosthesis. Artif. Organs. 27(11), 996–1004 (2003)
Luo, Y.H., da Cruz, L.: The Argus((R)) II retinal prosthesis system. Prog. Retin. Eye Res. 50, 89–107 (2016)
Deep, N.L., et al.: Cochlear implantation: an overview. J. Neurol. Surg. Part B, Skull Base. 80(2), 169–177 (2019)
Peng, K.A., et al.: Cochlear implantation and auditory brainstem implantation in neurofibromatosis type 2. Laryngoscope. 128(9), 2163–2169 (2018)
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)
Weintraub, K.: Aroma therapy. Sci. Am., 320(4), 8–10 (2019)
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)
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)
Hiremath, S.V., et al.: Human perception of electrical stimulation on the surface of somatosensory cortex. PLoS One. 12(5) (2017)
Charles, P.D., et al.: Predictors of effective bilateral subthalamic nucleus stimulation for PD. Neurology. 59(6), 932–934 (2002)
Lozano, A.M., et al.: Deep brain stimulation: current challenges and future directions. Nat. Rev. Neurol. 15(3), 148–160 (2019)
Akbari, H., et al.: Towards reconstructing intelligible speech from the human auditory cortex. Sci. Rep. 9(1), 874 (2019)
Anumanchipalli, G.K., Chartier, J., Chang, E.F.: Speech synthesis from neural decoding of spoken sentences. Nature. 568(7753), 493–498 (2019)
Hochberg, L.R., et al.: Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 485(7398), 372–375 (2012)
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)
Ben-Menachem, E.: Evaluation of refractory epilepsy treated with vagus never stimulation for up to 5 years. Neurology. 52, 1117–1118 (1999)
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)
Gummadavelli, A., et al.: Expanding brain-computer interfaces for controlling epilepsy networks: novel thalamic responsive neurostimulation in refractory epilepsy. Front. Neurosci. 12, 474 (2018)
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)
Rosin, B., et al.: Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron. 72(2), 370–384 (2011)
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)
Neumann, W.J., et al.: Toward electrophysiology-based intelligent adaptive deep brain stimulation for movement disorders. Neurotherapeutics. 16(1), 105–118 (2019)
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)
Priori, A., et al.: Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp. Neurol. 245, 77–86 (2013)
Berger, T.W., et al.: A cortical neural prosthesis for restoring and enhancing memory. J. Neural Eng. 8(4), 046017 (2011)
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)
Roskies, A.L.: Agency and intervention. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 370(1677), 20140215 (2015)
Rosenfeld, J.V., Broekmann, M.: Brain machine interface technology in neurosurgery. In: Ethics in neurosurgical practice. Cambridge University Press, Cambridge, UK (2019)
Vincent, C.: Auditory brainstem implants: how do they work? Anat. Rec. (Hoboken). 295(11), 1981–1986 (2012)
Otto, S.R., et al.: Multichannel auditory brainstem implant: update on performance in 61 patients. J. Neurosurg. 96(6), 1063–1071 (2002)
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)
Maynard, E.M.: Visual prostheses. Annu. Rev. Biomed. Eng. 3, 145–168 (2001)
Margalit, E., et al.: Retinal prosthesis for the blind. Surv. Ophthalmol. 47(4), 335–356 (2002)
Niketeghad, S., Pouratian, N.: Brain machine interfaces for vision restoration: the current state of cortical visual prosthetics. Neurotherapeutics. 16(1), 134–143 (2019)
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)
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)
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)
Najarpour Foroushani, A., Pack, C.C., Sawan, M.: Cortical visual prostheses: from microstimulation to functional percept. J. Neural Eng. 15(2), 021005 (2018)
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)
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)
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)
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)
Wang, W., et al.: An electrocorticographic brain interface in an individual with tetraplegia. PLoS One. 8(2), e55344 (2013)
Yanagisawa, T., et al.: Electrocorticographic control of a prosthetic arm in paralyzed patients. Ann. Neurol. 71(3), 353–361 (2012)
Even-Chen, N., et al.: Power-saving design opportunities for wireless intracortical brain–computer interfaces. Nature Biomed. Eng. 4, 984–996 (2020)
Bouton, C.E., et al.: Restoring cortical control of functional movement in a human with quadriplegia. Nature. 533(7602), 247–250 (2016)
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)
Oddo, C.M., et al.: Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans. elife. 5, e09148 (2016)
Martin, S., et al.: Decoding inner speech using electrocorticography: progress and challenges toward a speech prosthesis. Front. Neurosci. 12, 422 (2018)
Weiss, J.M., et al.: Artifact-free recordings in human bidirectional brain–computer interfaces. J. Neural Eng. 16(1), 016002 (2018)
Dexter, D.T., Jenner, P.: Parkinson disease: from pathology to molecular disease mechanisms. Free Radic. Biol. Med. 62, 132–144 (2013)
Kwan, P., Brodie, M.J.: Early identification of refractory epilepsy. N. Engl. J. Med. 342(5), 314–319 (2000)
Freestone, D.R., et al.: Seizure prediction: science fiction or soon to become reality? Curr. Neurol. Neurosci. Rep. 15(11), 73 (2015)
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)
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)
Strickland, E.: New startup aims to commercialise a brain prosthetic to improve memory. IEEE Spectr. (2016)
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)
Hong, G., et al.: Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity. Nano Lett. 15(10), 6979–6984 (2015)
Kim, T.I., et al.: Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science. 340(6129), 211–216 (2013)
Lee, S.W., et al.: Implantable microcoils for intracortical magnetic stimulation. Sci. Adv. 2(12), e1600889 (2016)
Martins, N.R.B., et al.: Human brain/cloud Interface. Front. Neurosci. 13, 112 (2019)
Author information
Authors and Affiliations
Corresponding author
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
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
-
(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.
-
(b)
The investigators also must sign a Declaration of Compliance with accepted ethical practices.
-
(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
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this entry
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