Bouton, C. E. et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 533, 247–250 (2016).
Kim, S.-P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P. & Black, M. J. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5, 455–476 (2008).
Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain-computer interface. eLife 6, e18554 (2017).
Simeral, J. D., Kim, S.-P., Black, M. J., Donoghue, J. P. & Hochberg, L. R. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J. Neural Eng. 8, 025027 (2011).
Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).
Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N. Engl. J. Med. 385, 217–227 (2021).
Wang, W. et al. An electrocorticographic brain interface in an individual with tetraplegia. PLoS ONE 8, e55344 (2013).
Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008).
Schwartz, A. B., Cui, X. T., Weber, D. J. & Moran, D. W. Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 52, 205–220 (2006).
Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013).
Brindley, G. S. & Lewin, W. S. The sensations produced by electrical stimulation of the visual cortex. J. Physiol. 196, 479–493 (1968).
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, 440–444 (1974).
Dobelle, W. H. et al. Artificial vision for the blind by electrical stimulation of the visual cortex. Neurosurgery 5, 521–527 (1979).
Eddington, D. K. Speech discrimination in deaf subjects with cochlear implants. J. Acoust. Soc. Am. 68, 885–891 (1980).
Wilson, B. S. & Dorman, M. F. Cochlear implants: a remarkable past and a brilliant future. Hear. Res. 242, 3–21 (2008).
Weiland, J. D., Liu, W. & Humayun, M. S. Retinal prosthesis. Annu. Rev. Biomed. Eng. 7, 361–401 (2005).
Lewis, P. M., Ackland, H. M., Lowery, A. J. & Rosenfeld, J. V. Restoration of vision in blind individuals using bionic devices: a review with a focus on cortical visual prostheses. Brain Res. 1595, 51–73 (2015).
Metzger, S. L. et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620, 1037–1046 (2023).
Willett, F. R. et al. A high-performance speech neuroprosthesis. Nature 620, 1031–1036 (2023).
Lorach, H. et al. Walking naturally after spinal cord injury using a brain–spine interface. Nature 618, 126–133 (2023).
Wang, W. et al. Human motor cortical activity recorded with micro-ECoG electrodes during individual finger movements. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 586–589 (2009).
Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).
Carmena, J. M. et al. Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol. 1, e42 (2003).
Lebedev, M. A. & Nicolelis, M. A. L. Brain–machine interfaces: past, present and future. Trends Neurosci. 29, 536–546 (2006).
Fraser, G. W., Chase, S. M., Whitford, A. & Schwartz, A. B. Control of a brain–computer interface without spike sorting. J. Neural Eng. 6, 055004 (2009).
Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752–1757 (2012).
Pandarinath, C. et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat. Methods 15, 805–815 (2018).
Collinger, J. L., Gaunt, R. A. & Schwartz, A. B. Progress towards restoring upper limb movement and sensation through intracortical brain-computer interfaces. Curr. Opin. Biomed. Eng. 8, 84–92 (2018).
Musk, E. & Neuralink An integrated brain-machine interface platform with thousands of channels. J. Med. Internet Res. 21, e16194 (2019).
Sahasrabuddhe, K. et al. The Argo: a high channel count recording system for neural recording in vivo. J. Neural. Eng. 18, 015002 (2021).
Schalk, G. et al. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J. Neural Eng. 4, 264–275 (2007).
Wissel, T. et al. Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography. J. Neural Eng. 10, 056020 (2013).
Moses, D. A., Leonard, M. K., Makin, J. G. & Chang, E. F. Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nat. Commun. 10, 3096 (2019).
Sun, P., Anumanchipalli, G. K. & Chang, E. F. Brain2Char: a deep architecture for decoding text from brain recordings. J. Neural. Eng. https://doi.org/10.1088/1741-2552/abc742 (2020).
Yu, B. M. et al. Mixture of trajectory models for neural decoding of goal-directed movements. J. Neurophysiol. 97, 3763–3780 (2007).
Kemere, C., Shenoy, K. V. & Meng, T. H. Model-based neural decoding of reaching movements: a maximum likelihood approach. IEEE Trans. Biomed. Eng. 51, 925–932 (2004).
Wilson, G. H. et al. Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus. J. Neural Eng. 17, 066007 (2020).
Harrison, R. R. & Charles, C. A low-power low-noise CMOS for amplifier neural recording applications. IEEE J. Solid-State Circuits 38, 958–965 (2003).
Harrison, R. R. et al. A low-power integrated circuit for a wireless 100-electrode neural recording system. IEEE J. Solid-State Circuits 42, 123–133 (2007).
Sarpeshkar, R. et al. Low-power circuits for brain–machine interfaces. IEEE Trans. Biomed. Circuits Syst. 2, 173–183 (2008).
Mora Lopez, C. et al. A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS. IEEE Trans. Biomed. Circuits Syst. 11, 510–522 (2017).
Wang, S. et al. A compact Quad-Shank CMOS neural probe with 5,120 addressable recording sites and 384 fully differential parallel channels. IEEE Trans. Biomed. Circuits Syst. 13, 1625–1634 (2019).
Biederman, W. et al. A 4.78 mm 2 fully-integrated neuromodulation SoC combining 64 acquisition channels with digital compression and simultaneous dual stimulation. IEEE J. Solid-State Circuits 50, 1038–1047 (2015).
Yoon, D.-Y. et al. A 1024-channel simultaneous recording neural SoC with stimulation and real-time spike detection. In 2021 Symposium on VLSI Circuits (ed Fukazawa, M.) 1–2 (IEEE, 2021).
Johnson, B. C. et al. An implantable 700 μW 64-channel neuromodulation IC for simultaneous recording and stimulation with rapid artifact recovery. In 2017 Symposium on VLSI Circuits (ed Tomita, Y.) C48–C49 (IEEE, 2017).
Lee, J. et al. Neural recording and stimulation using wireless networks of microimplants. Nat. Electron. 4, 604–614 (2021).
Ha, S. et al. Silicon-integrated high-density electrocortical interfaces. Proc. IEEE 105, 11–33 (2017).
Ahmadi, N. et al. Towards a distributed, chronically-implantable neural interface. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) 719–724 (IEEE, 2019).
Seo, D. et al. Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91, 529–539 (2016).
O’Leary, G., Groppe, D. M., Valiante, T. A., Verma, N. & Genov, R. NURIP: neural interface processor for brain-state classification and programmable-waveform neurostimulation. IEEE J. Solid-State Circuits 53, 3150–3162 (2018).
Cogan, S. F. Neural stimulation and recording electrodes. Annu. Rev. Biomed. Eng. 10, 275–309 (2008).
Wellman, S. M. et al. A materials roadmap to functional neural interface design. Adv. Funct. Mater. 28, 1701269 (2018).
Ordonez, J. S., Boehler, C., Schuettler, M. & Stieglitz, T. Improved polyimide thin-film electrodes for neural implants. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (eds Khoo, M. C. K., Cauwenberghs, G. & Weiland, J. D.) 5134–5137 (IEEE, 2012).
Vomero, M. et al. Incorporation of silicon carbide and diamond-like carbon as adhesion promoters improves In vitro and in vivo stability of thin-film glassy carbon electrocorticography Arrays. Adv. Biosys 2, 1700081 (2018).
Deku, F. et al. Amorphous silicon carbide ultramicroelectrode arrays for neural stimulation and recording. J. Neural Eng. 15, 016007 (2018).
Li, C. et al. Ultra-long-term reliable encapsulation using an atomic layer deposited HfO2/Al2O3/HfO2 triple-interlayer for biomedical implants. Coatings 9, 579 (2019).
Jeong, J. et al. Conformal hermetic sealing of wireless microelectronic implantable chiplets by multilayered atomic layer deposition (ALD). Adv. Funct. Mater. 29, 1806440 (2019).
Lamont, C. et al. Silicone encapsulation of thin-film SiOx, SiOxNy and SiC for modern electronic medical implants: a comparative long-term ageing study. J. Neural Eng. 18, 055003 (2021).
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, 563–571 (2013).
Smalley, E. The business of brain–computer interfaces. Nat. Biotechnol. 37, 978–982 (2019).
Lebedev, M. A. & Nicolelis, M. A. L. Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation. Physiol. Rev. 97, 767–837 (2017).
Kang, Y. et al. Epidemiology of worldwide spinal cord injury: a literature review. JN 6, 1–9 (2017).
Lo, J., Chan, L. & Flynn, S. A systematic review of the incidence, prevalence, costs and activity and work limitations of amputation, osteoarthritis, rheumatoid arthritis, back pain, multiple sclerosis. Spinal Cord Injury, Stroke and Traumatic Brain Injury in the United States: A 2019 Update. Arch. Phys. Med. Rehabilitation 102, 115–131 (2021).
Katan, M. & Luft, A. Global Burden of Stroke. Semin. Neurol. 38, 208–211 (2018).
Zack, M. M. & Kobau, R. National and state estimates of the numbers of adults and children with active epilepsy—United States, 2015. MMWR Morb. Mortal. Wkly. Rep. 66, 821–825 (2017).
Chan, T., Friedman, D. S., Bradley, C. & Massof, R. Estimates of incidence and prevalence of visual impairment, low vision, and blindness in the United States. JAMA Ophthalmol. 136, 12 (2018).
Matthews, K. A. et al. Racial and ethnic estimates of Alzheimer’s disease and related dementias in the United States (2015–2060) in adults aged ≥65 years. Alzheimer’s Dement. 15, 17–24 (2019).
Vomero, M. et al. Conformable polyimide-based μECoGs: bringing the electrodes closer to the signal source. Biomaterials 255, 120178 (2020).
Bockhorst, T. et al. Synchrony surfacing: epicortical recording of correlated action potentials. Eur. J. Neurosci. 48, 3583–3596 (2018).
Minev, I. R. et al. Electronic dura mater for long-term multimodal neural interfaces. Science 347, 159–163 (2015).
Viventi, J. et al. Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nat. Neurosci. 14, 1599–1605 (2011).
Khodagholy, D. et al. NeuroGrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–315 (2015).
Khodagholy, D. et al. Highly conformable conducting polymer electrodes for in vivo recordings. Adv. Mater. 23, H268–H272 (2011).
Woeppel, K. et al. Explant analysis of Utah electrode arrays implanted in human cortex for brain-computer-interfaces. Front. Bioeng. Biotechnol. 9, 759711 (2021).
Patel, P. R. et al. Utah array characterization and histological analysis of a multi-year implant in non-human primate motor and sensory cortices. J. Neural Eng. 20, 014001 (2023).
Szymanski, L. J. et al. Neuropathological effects of chronically implanted, intracortical microelectrodes in a tetraplegic patient. J. Neural Eng. 18, 0460b9 (2021).
Kim, B. J. et al. 3D parylene sheath neural probe for chronic recordings. J. Neural Eng. 10, 045002 (2013).
Chiang, C.-H. et al. Flexible, high-resolution thin-film electrodes for human and animal neural research. J. Neural Eng. 18, 045009 (2021).
Duraivel, S. et al. High-resolution neural recordings improve the accuracy of speech decoding. Nat. Commun. 14, 6938 (2023).
Hirschberg, A. W. et al. Development of an anatomically conformal parylene neural probe array for multi-region hippocampal recordings. In 2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS) 129–132 (IEEE, 2017).
Hara, S. A. et al. Long-term stability of intracortical recordings using perforated and arrayed parylene sheath electrodes. J. Neural Eng. 13, 066020 (2016).
Chung, J. E. et al. High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays. Neuron 101, 21–31.e5 (2019).
Kim, D.-H., Ghaffari, R., Lu, N. & Rogers, J. A. Flexible and stretchable electronics for biointegrated devices. Annu. Rev. Biomed. Eng. 14, 113–128 (2012).
Rubehn, B., Bosman, C., Oostenveld, R., Fries, P. & Stieglitz, T. A MEMS-based flexible multichannel ECoG-electrode array. J. Neural Eng. 6, 036003 (2009).
Salari, E. et al. Classification of articulator movements and movement direction from sensorimotor cortex activity. Sci. Rep. 9, 14165 (2019).
Schalk, G. et al. Two-dimensional movement control using electrocorticographic signals in humans. J. Neural Eng. 5, 75–84 (2008).
Pistohl, T., Ball, T., Schulze-Bonhage, A., Aertsen, A. & Mehring, C. Prediction of arm movement trajectories from ECoG-recordings in humans. J. Neurosci. Methods 167, 105–114 (2008).
Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568, 493–498 (2019).
Angrick, M. et al. Speech synthesis from ECoG using densely connected 3D convolutional neural networks. J. Neural Eng. 16, 036019 (2019).
Miller, K. J., Sorensen, L. B., Ojemann, J. G. & den Nijs, M. Power-law scaling in the brain surface electric potential. PLoS Comput. Biol. 5, e1000609 (2009).
Ho, E. et al. The Layer 7 Cortical Interface: a scalable and minimally invasive brain–computer interface platform. Preprint at bioRxiv https://doi.org/10.1101/2022.01.02.474656 (2022).
Konrad, P. et al. First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1024 electrodes. J. Neural Eng. 22, 026009 (2025).
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).
Biran, R., Martin, D. C. & Tresco, P. A. Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays. Exp. Neurol. 195, 115–126 (2005).
Winslow, B. D. & Tresco, P. A. Quantitative analysis of the tissue response to chronically implanted microwire electrodes in rat cortex. Biomaterials 31, 1558–1567 (2010).
Volkova, K., Lebedev, M. A., Kaplan, A. & Ossadtchi, A. Decoding movement from electrocorticographic activity: a review. Front. Neuroinform. 13, 74 (2019).
Branco, M. P. et al. Decoding hand gestures from primary somatosensory cortex using high-density ECoG. Neuroimage 147, 130–142 (2017).
Nakanishi, Y. et al. Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex. PLoS ONE 8, e72085 (2013).
Pistohl, T. et al. Grasp detection from human ECoG during natural reach-to-grasp movements. PLoS ONE 8, e54658 (2013).
Pistohl, T., Schulze-Bonhage, A., Aertsen, A., Mehring, C. & Ball, T. Decoding natural grasp types from human ECoG. Neuroimage 59, 248–260 (2012).
Makin, J. G., Moses, D. A. & Chang, E. F. Machine translation of cortical activity to text with an encoder–decoder framework. Nat. Neurosci. 23, 575–582 (2020).
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).
Tchoe, Y. et al. Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics. Sci. Transl. Med. 14, eabj1441 (2022).
Rachinskiy, I. et al. High-density, actively multiplexed µECoG array on reinforced silicone substrate. Front. Nanotechnol. 4, 837328 (2022).
Shi, Z. et al. Silk-enabled conformal multifunctional bioelectronics for investigation of spatiotemporal epileptiform activities and multimodal neural encoding/decoding. Adv. Sci. 6, 1801617 (2019).
Zhou, Y. et al. A silk-based self-adaptive flexible opto-electro neural probe. Microsyst. Nanoeng. 8, 118 (2022).
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. Nat. Neurosci. 5, 805–811 (2002).
Andersen, R. A., Musallam, S. & Pesaran, B. Selecting the signals for a brain–machine interface. Curr. Opin. Neurobiol. 14, 720–726 (2004).
Scherberger, H., Jarvis, M. R. & Andersen, R. A. Cortical local field potential encodes movement intentions in the posterior parietal cortex. Neuron 46, 347–354 (2005).
Zhuang, J., Truccolo, W., Vargas-Irwin, C. & Donoghue, J. P. Decoding 3-D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex. IEEE Trans. Biomed. Eng. 57, 1774–1784 (2010).
Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E. & Donoghue, J. P. Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. J. Neurophysiol. 107, 1337–1355 (2012).
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).
Herron, J. et al. Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation. Nat. Biomed. Eng. 9, 606–617 (2025).
Source link