Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Translational opportunities and challenges of invasive electrodes for neural interfaces

Abstract

Invasive brain–machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Neural signals and traditional probe architectures.
Fig. 2: Electrode–tissue interface and electrode coatings.
Fig. 3: Abiotic failure modes.
Fig. 4: Biotic failure modes.
Fig. 5: Ultrasmall and ultraflexible electrodes.
Fig. 6: Electrode architectures designed for tissue integration.
Fig. 7: High-channel-count recording technologies.

Similar content being viewed by others

References

  1. Lebedev, M. A. & Nicolelis, M. A. L. Brain–machine interfaces: from basic science to neuroprostheses and neurorehabilitation. Physiol. Rev. 97, 767–837 (2017).

    Article  PubMed  Google Scholar 

  2. Min, B.-K., Marzelli, M. J. & Yoo, S.-S. Neuroimaging-based approaches in the brain–computer interface. Trends Biotechnol. 28, 552–560 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Nicolas-Alonso, L. F. & Gomez-Gil, J. Brain computer interfaces, a review. Sensors 12, 1211–1279 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  4. Naseer, N. & Hong, K.-S. fNIRS-based brain-computer interfaces: a review. Front. Hum. Neurosci. 9, 3 (2015).

    PubMed Central  PubMed  Google Scholar 

  5. Hong, K.-S., Ghafoor, U. & Khan, M. J. Brain–machine interfaces using functional near-infrared spectroscopy: a review. Artif. Life Robot. 25, 204–218 (2020).

    Article  Google Scholar 

  6. Thibault, R. T., MacPherson, A., Lifshitz, M., Roth, R. R. & Raz, A. Neurofeedback with fMRI: a critical systematic review. Neuroimage 172, 786–807 (2018).

    Article  PubMed  Google Scholar 

  7. Tonin, L. & Millán, Jd. R. Noninvasive brain–machine interfaces for robotic devices. Annu. Rev. Control Robot. Auton. Syst. 4, 191–214 (2020).

    Article  Google Scholar 

  8. Bullard, J., Hutchison, B. C., Lee, J., Chestek, C. A. & Patil, P. G. Estimating risk for future intracranial, fully implanted, modular neuroprosthetic systems: a systematic review of hardware complications in clinical deep brain stimulation and experimental human intracortical arrays. Neuromodulation 23, 411–426 (2019).

    Article  PubMed  Google Scholar 

  9. Anderson, D. N., Osting, B., Vorwerk, J., Dorval, A. D. & Butson, C. R. Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. J. Neural Eng. 15, 026005 (2018).

    Article  PubMed  Google Scholar 

  10. Skarpaas, T. L., Jarosiewicz, B. & Morrell, M. J. Brain-responsive neurostimulation for epilepsy (RNS® System). Epilepsy Res. 153, 68–70 (2019).

    Article  PubMed  Google Scholar 

  11. Stieglitz, T. Of man and mice: translational research in neurotechnology. Neuron 105, 12–15 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  13. Won, S. M., Song, E., Reeder, J. T. & Rogers, J. A. Emerging modalities and implantable technologies for neuromodulation. Cell 181, 115–135 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Chen, R., Canales, A. & Anikeeva, P. Neural recording and modulation technologies. Nat. Rev. Mater. 2, 16093 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Frank, J. A., Antonini, M.-J. & Anikeeva, P. Next-generation interfaces for studying neural function. Nat. Biotechnol. 37, 1013–1023 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Wellman, S. M. et al. A materials roadmap to functional neural interface design. Adv. Funct. Mater. 28, 1701269 (2018).

    Article  PubMed  Google Scholar 

  17. Hong, G. & Lieber, C. M. Novel electrode technologies for neural recordings. Nat. Rev. Neurosci. 20, 330–345 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Pei, F. & Tian, B. Nanoelectronics for minimally invasive cellular recordings. Adv. Funct. Mater. 30, 1906210 (2019).

    Article  Google Scholar 

  19. Abbott, J., Ye, T., Ham, D. & Park, H. Optimizing nanoelectrode arrays for scalable intracellular electrophysiology. Acc. Chem. Res. 51, 600–608 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Annecchino, L. A. & Schultz, S. R. Progress in automating patch clamp cellular physiology. Brain Neurosci. Adv. 2, 2398212818776561 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  21. Zhang, A., Zhao, Y., You, S. S. & Lieber, C. M. Nanowire probes could drive high-resolution brain-machine interfaces. Nano Today 31, 100821 (2020).

    Article  CAS  Google Scholar 

  22. Jouhanneau, J.-S., Kremkow, J., Dorrn, A. L. & Poulet, J. F. A. In vivo monosynaptic excitatory transmission between layer 2 cortical pyramidal neurons. Cell Rep. 13, 2098–2106 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Kodandaramaiah, S. B., Franzesi, G. T., Chow, B. Y., Boyden, E. S. & Forest, C. R. Automated whole-cell patch-clamp electrophysiology of neurons in vivo. Nat. Methods 9, 585–587 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Kodandaramaiah, S. B. et al. Multi-neuron intracellular recording in vivo via interacting autopatching robots. eLife 7, e24656 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  25. Holst, G. L. et al. Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex. J. Neurophysiol. 121, 2341–2357 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  26. Dubey, A. & Ray, S. Cortical electrocorticogram (ECoG) is a local signal. J. Neurosci. 39, 4299–4311 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Yanagisawa, T. et al. Real-time control of a prosthetic hand using human electrocorticography signals. J. Neurosurg. 114, 1715–1722 (2011).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Nune, G. et al. Treatment of drug-resistant epilepsy in patients with periventricular nodular heterotopia using RNS® System: efficacy and description of chronic electrophysiological recordings. Clin. Neurophysiol. 130, 1196–1207 (2019).

    Article  PubMed  Google Scholar 

  30. Vansteensel, M. J. et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N. Engl. J. Med. 375, 2060–2066 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  31. Khodagholy, D. et al. NeuroGrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–315 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Khodagholy, D. et al. Organic electronics for high-resolution electrocorticography of the human brain. Sci. Adv. 2, e1601027 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  33. Ledochowitsch, P. et al. Fabrication and testing of a large area, high density, parylene MEMS μECoG array. In IEEE 24th International Conference on Micro Electro Mechanical Systems 1031–1034 (IEEE, 2011).

  34. Muller, L. et al. Thin-film, high-density micro-electrocorticographic decoding of a human cortical gyrus. In 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 1528–1531 (IEEE, 2016).

  35. Jackson, A. & Fetz, E. E. Compact movable microwire array for long-term chronic unit recording in cerebral cortex of primates. J. Neurophysiol. 98, 3109–3118 (2007).

    Article  PubMed  Google Scholar 

  36. Williams, J. C., Rennaker, R. L. & Kipke, D. R. Long-term neural recording characteristics of wire microelectrode arrays implanted in cerebral cortex. Brain Res. Protoc. 4, 303–313 (1999).

    Article  CAS  Google Scholar 

  37. Ferguson, J. E., Boldt, C. & Redish, A. D. Creating low-impedance tetrodes by electroplating with additives. Sens. Actuators A 156, 388–393 (2009).

    Article  CAS  Google Scholar 

  38. Schwarz, D. A. et al. Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nat. Methods 11, 670–676 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Kollo, M. et al. CHIME: CMOS-hosted in-vivo microelectrodes for massively scalable neuronal recordings. Front. Neurosci. https://doi.org/10.3389/fnins.2020.00834 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  40. Obaid, A. et al. Massively parallel microwire arrays integrated with CMOS chips for neural recording. Sci. Adv. 6, eaay2789 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Kipke, D. R., Vetter, R. J., Williams, J. C. & Hetke, J. F. Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 151–155 (2003).

    Article  PubMed  Google Scholar 

  42. Jones, K. E., Campbell, K. & Normann, R. A. A glass/silicon composite intracortical electrode array. Ann. Biomed. Eng. 20, 423–437 (1992).

    Article  CAS  PubMed  Google Scholar 

  43. Li, Z. Decoding methods for neural prostheses: where have we reached? Front. Syst. Neurosci. 8, 129 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  44. Kao, J. C., Stavisky, S. D., Sussillo, D., Nuyujukian & Shenoy, K. V. Information systems opportunities in brain–machine interface decoders. Proc. IEEE 102, 666–682 (2014).

    Article  Google Scholar 

  45. Shenoy, K. V. & Carmena, J. M. Combining decoder design and neural adaptation in brain–machine interfaces. Neuron 84, 665–680 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. Pandarinath, C. et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat. Methods 15, 805–815 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. Turner, J. N. et al. Cerebral astrocyte response to micromachined silicon implants. Exp. Neurol. 156, 33–49 (1999).

    Article  CAS  PubMed  Google Scholar 

  48. 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).

    Article  CAS  PubMed  Google Scholar 

  49. Lebedev, M. A., Crist, R. E. & Nicolelis, M. A. L. in Methods for Neural Ensemble Recordings 2nd edn (ed Nicolelis, M. A. L.) Ch. 11 (CRC Press/Taylor & Francis, 2007).

  50. Miller, E. K., Lundqvist, M. & Bastos, A. M. Working Memory 2.0. Neuron 100, 463–475 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  52. Ajiboye, A. B. et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet 389, 1821–1830 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  53. Nuyujukian, P. et al. Cortical control of a tablet computer by people with paralysis. PLoS ONE 13, e0204566 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  54. Degenhart, A. D. et al. Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity. Nat. Biomed. Eng. 4, 672–685 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  55. Merrill, D. R., Bikson, M. & Jefferys, J. G. R. Electrical stimulation of excitable tissue: design of efficacious and safe protocols. J. Neurosci. Methods 141, 171–198 (2005).

    Article  PubMed  Google Scholar 

  56. Robinson, J. T. et al. Developing next-generation brain sensing technologies–a review. IEEE Sens. J. 19, 10163–10175 (2019).

    Article  CAS  Google Scholar 

  57. Cogan, S. F. Microelectrode coatings for neural stimulation and recording. In Proc. 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 3798–3801 (IEEE, 2003).

  58. Chouard, C. H. & Pialoux, P. Biocompatibility of cochlear implants. Bull. Acad. Natl Med. 179, 549–555 (1995).

    CAS  PubMed  Google Scholar 

  59. Majji, A. B. et al. Long-term histological and electrophysiological results of an inactive epiretinal electrode array implantation in dogs. Invest. Ophthalmol. Vis. Sci. 40, 2073–2081 (1999).

    CAS  PubMed  Google Scholar 

  60. Stronks, H. C. & Dagnelie, G. The functional performance of the Argus II retinal prosthesis. Expert Rev. Med. Devices 11, 23–30 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Sun, F. T. & Morrell, M. J. The RNS System: responsive cortical stimulation for the treatment of refractory partial epilepsy. Expert Rev. Med. Devices 11, 563–572 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Musk, E. et al. An integrated brain-machine interface platform with thousands of channels. J. Med. Internet Res. 21, e16194 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  63. Keefer, E. W., Botterman, B. R., Romero, M. I., Rossi, A. F. & Gross, G. W. Carbon nanotube coating improves neuronal recordings. Nat. Nanotechnol. 3, 434–439 (2008).

    Article  CAS  PubMed  Google Scholar 

  64. Venkatraman, S. et al. In vitro and in vivo evaluation of PEDOT microelectrodes for neural stimulation and recording. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 307–316 (2011).

    Article  PubMed  Google Scholar 

  65. Bédard, C., Kröger, H. & Destexhe, A. Modeling extracellular field potentials and the frequency-filtering properties of extracellular space. Biophys. J. 86, 1829–1842 (2004).

    Article  PubMed Central  PubMed  Google Scholar 

  66. Marblestone, A. et al. Physical principles for scalable neural recording. Front. Comput. Neurosci. 7, 137 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  67. Kleinfeld, D. et al. Can one concurrently record electrical spikes from every neuron in a mammalian brain? Neuron 103, 1005–1015 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  68. Robinson, D. A. The electrical properties of metal microelectrodes. Proc. IEEE 56, 1065–1071 (1968).

    Article  CAS  Google Scholar 

  69. Nyquist, H. Thermal agitation of electric charge in conductors. Phys. Rev. 32, 110 (1928).

    Article  CAS  Google Scholar 

  70. Hassibi, A., Navid, R., Dutton, R. W. & Lee, T. H. Comprehensive study of noise processes in electrode electrolyte interfaces. J. Appl. Phys. 96, 1074–1082 (2004).

    Article  CAS  Google Scholar 

  71. Grill, W. M. Safety considerations for deep brain stimulation: review and analysis. Expert Rev. Med. Devices 2, 409–420 (2005).

    Article  PubMed  Google Scholar 

  72. Hudak, E. M., Mortimer, J. T. & Martin, H. B. Platinum for neural stimulation: voltammetry considerations. J. Neural Eng. 7, 026005 (2010).

    Article  CAS  Google Scholar 

  73. Cogan, S. F., Ludwig, K. A., Welle, C. G. & Takmakov, P. Tissue damage thresholds during therapeutic electrical stimulation. J. Neural Eng. 13, 021001 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  74. Cogan, S. F., Hara, S. & Ludwig, K. A. in Neuromodulation (eds Krames, E. S. et al.) 83–94 (Elsevier, 2018).

  75. Huang, C. Q., Carter, M. & Shepherd, R. K. Stimulus induced pH changes in cochlear implants: an in vitro and in vivo study. Ann. Biomed. Eng. 29, 791–802 (2001).

    Article  CAS  PubMed  Google Scholar 

  76. Crago, P. E., Peckham, H., Mortimer, J. T. & Van Der Meulen, J. P. The choice of pulse duration for chronic electrical stimulation via surface, nerve, and intramuscular electrodes. Ann. Biomed. Eng. 2, 252–264 (1974).

    Article  CAS  PubMed  Google Scholar 

  77. Grill, W. M. & Mortimer, J. T. The effect of stimulus pulse duration on selectivity of neural stimulation. IEEE Trans. Biomed. Eng. 43, 161–166 (1996).

    Article  PubMed  Google Scholar 

  78. Robblee, L. S., McHardy, J., Agnew, W. F. & Bullara, L. A. Electrical stimulation with Pt electrodes. VII. Dissolution of Pt electrodes during electrical stimulation of the cat cerebral cortex. J. Neurosci. Methods 9, 301–308 (1983).

    Article  CAS  PubMed  Google Scholar 

  79. Brummer, S. B. & Turner, M. J. Electrochemical considerations for safe electrical stimulation of the nervous system with platinum electrodes. IEEE Trans. Biomed. Eng. BME-24, 59–63 (1977).

    Article  CAS  Google Scholar 

  80. McHardy, J., Robblee, L. S., Marston, J. M. & Brummer, S. B. Electrical stimulation with Pt electrodes. IV. Factors influencing Pt dissolution in inorganic saline. Biomaterials 1, 129–134 (1980).

    Article  CAS  PubMed  Google Scholar 

  81. Guyton, D. L. & Hambrecht, F. T. Theory and design of capacitor electrodes for chronic stimulation. Med. Biol. Eng. 12, 613–620 (1974).

    Article  CAS  PubMed  Google Scholar 

  82. Agnew, W. F., Yuen, T. G. H., McCreery, D. B. & Bullara, L. A. Histopathologic evaluation of prolonged intracortical electrical stimulation. Exp. Neurol. 92, 162–185 (1986).

    Article  CAS  PubMed  Google Scholar 

  83. Lempka, S. F., Johnson, M. D., Miocinovic, S., Vitek, J. L. & McIntyre, C. C. Current-controlled deep brain stimulation reduces in vivo voltage fluctuations observed during voltage-controlled stimulation. Clin. Neurophysiol. 121, 2128–2133 (2010).

    Article  PubMed Central  PubMed  Google Scholar 

  84. Zeng, F.-G., Rebscher, S., Harrison, W., Sun, X. & Feng, H. Cochlear implants: system design, integration, and evaluation. IEEE Rev. Biomed. Eng. 1, 115–142 (2008).

    Article  PubMed Central  PubMed  Google Scholar 

  85. Salas, M. A. et al. Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation. eLife 7, e32904 (2018).

    Article  Google Scholar 

  86. Zhou, D. D., Dorn, J. D. & Greenberg, R. J. The Argus® II retinal prosthesis system: an overview’. In IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 1–6 (IEEE, 2013).

  87. Hambrecht, F. T. Visual prostheses based on direct interfaces with the visual system. Baillieres Clin. Neurol. 4, 147–165 (1995).

    CAS  PubMed  Google Scholar 

  88. Leung, R. T., Shivdasani, M. N., Nayagam, D. A. X. & Shepherd, R. K. In vivo and in vitro comparison of the charge injection capacity of platinum macroelectrodes. IEEE Trans. Biomed. Eng. 62, 849–857 (2014).

    Article  PubMed  Google Scholar 

  89. Kane, S. R. et al. Electrical performance of penetrating microelectrodes chronically implanted in cat cortex. IEEE Trans. Biomed. Eng. 60, 2153–2160 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  90. Ludwig, K. A. et al. Poly (3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodes. J. Neural Eng. 8, 014001 (2011).

    Article  PubMed Central  PubMed  Google Scholar 

  91. Cui, X. & Martin, D. C. Electrochemical deposition and characterization of poly (3,4-ethylenedioxythiophene) on neural microelectrode arrays. Sens. Actuators B 89, 92–102 (2003).

    Article  CAS  Google Scholar 

  92. Thaning, E. M., Asplund, M. L. M., Nyberg, T. A., Inganäs, O. W. & Holst, H. Stability of poly (3,4-ethylene dioxythiophene) materials intended for implants. J. Biomed. Mater. Res. B 93, 407–415 (2010).

    Article  Google Scholar 

  93. Leber, M. et al. Long term performance of porous platinum coated neural electrodes. Biomed. Microdevices 19, 62 (2017).

    Article  CAS  PubMed  Google Scholar 

  94. Aryan, N. P. et al. In vitro study of titanium nitride electrodes for neural stimulation. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2866–2869 (IEEE, 2011).

  95. Cogan, S. F., Guzelian, A. A., Agnew, W. F., Yuen, T. G. H. & McCreery, D. B. Over-pulsing degrades activated iridium oxide films used for intracortical neural stimulation. J. Neurosci. Methods 137, 141–150 (2004).

    Article  CAS  PubMed  Google Scholar 

  96. Salatino, J. W., Ludwig, K. A., Kozai, T. D. Y. & Purcell, E. K. Glial responses to implanted electrodes in the brain. Nat. Biomed. Eng. 1, 862–877 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  97. Jorfi, M., Skousen, J. L., Weder, C. & Capadona, J. R. Progress towards biocompatible intracortical microelectrodes for neural interfacing applications. J. Neural Eng. 12, 011001 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  98. Kuliasha, C. A. & Judy, J. W. In vitro reactive-accelerated-aging assessment of anisotropic conductive adhesive and back-end packaging for electronic neural interfaces. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 3766–3769 (IEEE, 2019).

  99. Patrick, E., Orazem, M. E., Sanchez, J. C. & Nishida, T. Corrosion of tungsten microelectrodes used in neural recording applications. J. Neurosci. Methods 198, 158–171 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  100. Bowman, L. & Meindl, J. D. The packaging of implantable integrated sensors. IEEE Trans. Biomed. Eng. 33, 248–255 (1986).

    Article  CAS  PubMed  Google Scholar 

  101. 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, 026003 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  102. Prasad, A. et al. Comprehensive characterization and failure modes of tungsten microwire arrays in chronic neural implants. J. Neural Eng. 9, 056015 (2012).

    Article  PubMed  Google Scholar 

  103. Prasad, A. et al. Abiotic-biotic characterization of Pt/Ir microelectrode arrays in chronic implants. Front. Neuroeng. 7, 2 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  104. Ordonez, J. S., Boehler, C., Schuettler, M. & Stieglitz, T. Improved polyimide thin-film electrodes for neural implants. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society 5134–5137 (IEEE, 2012).

  105. Ordonez, J. S., Boehler, C., Schuettler, M. & Stieglitz, T. Long-term adhesion studies of polyimide to inorganic and metallic layers. MRS Online Proc. Library https://doi.org/10.1557/opl.2012.1198 (2012).

    Article  Google Scholar 

  106. Ordonez, J. S., Boehler, C., Schuettler, M. & Stieglitz, T. Silicone rubber and thin-film polyimide for hybrid neural interfaces—a MEMS-based adhesion promotion technique. In 6th International IEEE/EMBS Conference on Neural Engineering (NER) 872–875 (IEEE, 2013).

  107. Kuliasha, C. A. & Judy, J. W. In vitro reactive-accelerated-aging (RAA) assessment of tissue-engineered electronic nerve interfaces (TEENI). In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 5061–5064 (IEEE, 2018).

  108. Loeb, G. E., Bak, M. J., Salcman, M. & Schmidt, E. M. Parylene as a chronically stable, reproducible microelectrode insulator. IEEE Trans. Biomed. Eng. 24, 121–128 (1977).

    Article  CAS  PubMed  Google Scholar 

  109. Traeger, R. Nonhermeticity of polymeric lid sealants. IEEE Trans. Parts Hybrids Packaging 13, 147–152 (1977).

    Article  Google Scholar 

  110. Vanhoestenberghe, A. & Donaldson, N. Corrosion of silicon integrated circuits and lifetime predictions in implantable electronic devices. J. Neural Eng. 10, 031002 (2013).

    Article  CAS  PubMed  Google Scholar 

  111. Hassler, C., Metzen, R. P., Ruther & Stieglitz, T. Characterization of parylene C as an encapsulation material for implanted neural prostheses. J. Biomed. Mater. Res. B 93, 266–274 (2010).

    Google Scholar 

  112. Von Metzen, R. P. & Stieglitz, T. The effects of annealing on mechanical, chemical, and physical properties and structural stability of parylene C. Biomed. Microdevices 15, 727–735 (2013).

    Article  CAS  Google Scholar 

  113. Kim, B. J., Washabaugh, E. P. & Meng, E. Annealing effects on flexible multi-layered parylene-based sensors. In IEEE 27th International Conference on Micro Electro Mechanical Systems (MEMS) 825–828 (IEEE, 2014).

  114. Gwon, T. M., Kim, J. H., Choi, G. J. & Kim, S. J. Mechanical interlocking to improve metal–polymer adhesion in polymer-based neural electrodes and its impact on device reliability. J. Mater. Sci. 51, 6897–6912 (2016).

    Article  CAS  Google Scholar 

  115. Kim, W.-S., Yun, I.-H., Lee, J.-J. & Jung, H.-T. Evaluation of mechanical interlock effect on adhesion strength of polymer–metal interfaces using micro-patterned surface topography. Int. J. Adhes. Adhes. 30, 408–417 (2010).

    Article  CAS  Google Scholar 

  116. Fang, H. et al. Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems. Proc. Natl Acad. Sci. USA 113, 11682–11687 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  117. Xie, X. et al. Long-term reliability of Al2O3 and parylene C bilayer encapsulated Utah electrode array based neural interfaces for chronic implantation. J. Neural Eng. 11, 026016 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  118. Maloney, J. M., Lipka, S. A. & Baldwin, S. P. In vivo biostability of CVD silicon oxide and silicon nitride films. MRS Online Proc. Library 872, 143 (2005).

    Google Scholar 

  119. Jeong, J. et al. Conformal hermetic sealing of wireless microelectronic implantable chiplets by multilayered atomic layer deposition (ALD). Adv. Funct. Mater. 29, 1806440 (2019).

    Article  Google Scholar 

  120. Cogan, S. F., Edell, D. J., Guzelian, A. A., Ping Liu, Y. & Edell, R. Plasma-enhanced chemical vapor deposited silicon carbide as an implantable dielectric coating. J. Biomed. Mater. Res. A 67, 856–867 (2003).

    Article  PubMed  Google Scholar 

  121. Hsu, J.-M., Tathireddy, Rieth, L., Normann, A. R. & Solzbacher, F. Characterization of a-SiCx:H thin films as an encapsulation material for integrated silicon based neural interface devices. Thin Solid Films 516, 34–41 (2007).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  122. Lei, X. et al. SiC protective coating for photovoltaic retinal prosthesis. J. Neural Eng. 13, 046016 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  123. Knaack, G. L. et al. In vivo characterization of amorphous silicon carbide as a biomaterial for chronic neural interfaces. Front. Neurosci. 10, 301 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  124. Phan, H.-P. et al. Long-lived, transferred crystalline silicon carbide nanomembranes for implantable flexible electronics. ACS Nano 13, 11572–11581 (2019).

    Article  CAS  PubMed  Google Scholar 

  125. Diaz-Botia, C. A. et al. A silicon carbide array for electrocorticography and peripheral nerve recording. J. Neural Eng. 14, 056006 (2017).

    Article  CAS  PubMed  Google Scholar 

  126. Beygi, M. et al. Fabrication of a monolithic implantable neural interface from cubic silicon carbide. Micromachines 10, 430 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  128. Saxena, T. et al. The impact of chronic blood–brain barrier breach on intracortical electrode function. Biomaterials 34, 4703–4713 (2013).

    Article  CAS  PubMed  Google Scholar 

  129. Kozai, T. D. Y., Jaquins-Gerstl, A. S., Vazquez, A. L., Michael, A. C. & Cui, X. T. Brain tissue responses to neural implants impact signal sensitivity and intervention strategies. ACS Chem. Neurosci. 6, 48–67 (2015).

    Article  CAS  PubMed  Google Scholar 

  130. Kozai, T. D. Y., Vazquez, A. L., Weaver, C. L., Kim, S.-G. & Cui, X. T. In vivo two-photon microscopy reveals immediate microglial reaction to implantation of microelectrode through extension of processes. J. Neural Eng. 9, 066001 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  131. Seymour, J. P. & Kipke, D. R. Neural probe design for reduced tissue encapsulation in CNS. Biomaterials 28, 3594–3607 (2007).

    Article  CAS  PubMed  Google Scholar 

  132. Skousen, J. L. et al. Reducing surface area while maintaining implant penetrating profile lowers the brain foreign body response to chronically implanted planar silicon microelectrode arrays. Prog. Brain Res. 194, 167–180 (2011).

    Article  PubMed  Google Scholar 

  133. Sanders, J. E., Stiles, C. E. & Hayes, C. L. Tissue response to single-polymer fibers of varying diameters: evaluation of fibrous encapsulation and macrophage density. J. Biomed. Mater. Res. 52, 231–237 (2000).

    Article  CAS  PubMed  Google Scholar 

  134. Yang, Q. et al. Zwitterionic polymer coating suppresses microglial encapsulation to neural implants in vitro and in vivo. Adv. Biosyst. 4, 1900287 (2020).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  136. Fu, T.-M. et al. Stable long-term chronic brain mapping at the single-neuron level. Nat. Methods 13, 875–882 (2016).

    Article  CAS  PubMed  Google Scholar 

  137. Zhou, T. et al. Syringe-injectable mesh electronics integrate seamlessly with minimal chronic immune response in the brain. Proc. Natl Acad. Sci. USA 114, 5894–5899 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  138. Biran, R., Martin, D. C. & Tresco, P. A. The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull. J. Biomed. Mater. Res. A 82, 169–178 (2007).

    Article  PubMed  Google Scholar 

  139. Shen, K. & Maharbiz, M. M. Ceramic packaging in neural implants. J. Neural Eng. 18, 025002 (2021).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  141. 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).

    Article  PubMed Central  PubMed  Google Scholar 

  142. Kim, S.-P. et al. Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 193–203 (2011).

    Article  PubMed Central  PubMed  Google Scholar 

  143. 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).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  145. Kim, S. et al. Integrated wireless neural interface based on the Utah electrode array. Biomed. Microdevices 11, 453–466 (2009).

    Article  CAS  PubMed  Google Scholar 

  146. Rios, G., Lubenov, E. V., Chi, D., Roukes, M. L. & Siapas, A. G. Nanofabricated neural probes for dense 3-D recordings of brain activity. Nano Lett. 16, 6857–6862 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  147. Seymour, J. P., Wu, F., Wise, K. D. & Yoon, E. State-of-the-art MEMS and microsystem tools for brain research. Microsyst. Nanoeng. 3, 16066 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  148. Massey, T. L. et al. A high-density carbon fiber neural recording array technology. J. Neural Eng. 16, 016024 (2019).

    Article  PubMed  Google Scholar 

  149. Gillis, W. F. et al. Carbon fiber on polyimide ultra-microelectrodes. J. Neural Eng. 15, 016010 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  150. Guitchounts, G., Markowitz, J. E., Liberti, W. A. & Gardner, T. J. A carbon-fiber electrode array for long-term neural recording. J. Neural Eng. 10, 046016 (2013).

    Article  PubMed  Google Scholar 

  151. Patel, P. R. et al. Insertion of linear 8.4 μm diameter 16 channel carbon fiber electrode arrays for single unit recordings. J. Neural Eng. 12, 046009 (2015).

    Article  PubMed Central  PubMed  Google Scholar 

  152. Patel, P. R. et al. Chronic in vivo stability assessment of carbon fiber microelectrode arrays. J. Neural Eng. 13, 066002 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  153. Luan, L. et al. Ultraflexible nanoelectronic probes form reliable, glial scar–free neural integration. Sci. Adv. 3, e1601966 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  154. Wei, X. et al. Nanofabricated ultraflexible electrode arrays for high-density intracortical recording. Adv. Sci. 5, 1700625 (2018).

    Article  Google Scholar 

  155. Hanson, T. L., Diaz-Botia, C. A., Kharazia, V., Maharbiz, M. M. & Sabes, P. N. The “sewing machine” for minimally invasive neural recording. Preprint at bioRxiv https://doi.org/10.1101/578542 (2019).

    Article  Google Scholar 

  156. Du, Z. J. et al. Ultrasoft microwire neural electrodes improve chronic tissue integration. Acta Biomater. 53, 46–58 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  157. Ferro, M. D. et al. NeuroRoots, a bio-inspired, seamless Brain Machine Interface device for long-term recording. Preprint at bioRxiv https://doi.org/10.1101/460949 (2018).

  158. Na, K. et al. Novel diamond shuttle to deliver flexible neural probe with reduced tissue compression. Microsyst. Nanoeng. 6, 37 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  159. Chen, P.-C. & Lal, A. Detachable ultrasonic enabled inserter for neural probe insertion using biodissolvable polyethylene glycol. In 2015 Transducers—2015 18th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS) 125–128 (IEEE, 2015).

  160. Barz, F., Ruther, Takeuchi, S. & Paul, O. Flexible silicon-polymer neural probe rigidified by dissolvable insertion vehicle for high-resolution neural recording with improved duration. In 28th IEEE International Conference on Micro Electro Mechanical Systems (MEMS) 636–639 (IEEE, 2015).

  161. Ceyssens, F. et al. Chronic neural recording with probes of subcellular cross-section using 0.06 mm² dissolving microneedles as insertion device. Sens. Actuators B 284, 369–376 (2019).

    Article  CAS  Google Scholar 

  162. Kim, D.-H. et al. Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics. Nat. Mater. 9, 511–517 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  163. Piore, A. To study the brain, a doctor puts himself under the knife. MIT Technology Review https://www.technologyreview.com/2015/11/09/247535/to-study-the-brain-a-doctor-puts-himself-under-the-knife/ (2015).

  164. Kennedy, P. R. The cone electrode: a long-term electrode that records from neurites grown onto its recording surface. J. Neurosci. Methods 29, 181–193 (1989).

    Article  CAS  PubMed  Google Scholar 

  165. Bartels, J. et al. Neurotrophic electrode: method of assembly and implantation into human motor speech cortex. J. Neurosci. Methods 174, 168–176 (2008).

    Article  PubMed Central  PubMed  Google Scholar 

  166. Gearing, M. & Kennedy, P. Histological confirmation of myelinated neural filaments within the tip of the neurotrophic electrode after a decade of neural recordings. Front. Hum. Neurosci. 14, 111 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  167. Xie, C. et al. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes. Nat. Mater. 14, 1286–1292 (2015).

    Article  CAS  PubMed  Google Scholar 

  168. Yang, X. et al. Bioinspired neuron-like electronics. Nat. Mater. 18, 510–517 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  169. Olsson, R. H. & Wise, K. D. A three-dimensional neural recording microsystem with implantable data compression circuitry. IEEE J. Solid State Circuits 40, 2796–2804 (2005).

    Article  Google Scholar 

  170. Ruther, P. & Paul, O. New approaches for CMOS-based devices for large-scale neural recording. Curr. Opin. Neurobiol. 32, 31–37 (2015).

    Article  CAS  PubMed  Google Scholar 

  171. Ng, K. A., Greenwald, E., Xu, Y. P. & Thakor, N. V. Implantable neurotechnologies: a review of integrated circuit neural amplifiers. Med. Biol. Eng. Comput. 54, 45–62 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  172. Wang, P.-M. et al. in Interfacing Bioelectronics and Biomedical Sensing (eds Cao, H. et al.) 1–28 (Springer, 2020).

  173. Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  174. Raducanu, B. C. et al. Time multiplexed active neural probe with 1356 parallel recording sites. Sensors 17, 2388 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  175. Chung, J. E. et al. High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays. Neuron 101, 21–31 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  177. Patolsky, F. et al. Detection, stimulation, and inhibition of neuronal signals with high-density nanowire transistor arrays. Science 313, 1100–1104 (2006).

    Article  CAS  PubMed  Google Scholar 

  178. Steinmetz, N. A., Koch, C., Harris, K. D. & Carandini, M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr. Opin. Neurobiol. 50, 92–100 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  179. Borton, D. A., Yin, M., Aceros, J. & Nurmikko, A. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J. Neural Eng. 10, 026010 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  180. Muller, R. et al. A minimally invasive 64-channel wireless μECoG implant. IEEE J. Solid State Circuits 50, 344–359 (2014).

    Article  Google Scholar 

  181. Thelin, J. et al. Implant size and fixation mode strongly influence tissue reactions in the CNS. PLoS ONE 6, e16267 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  182. Leung, V. W. et al. A CMOS distributed sensor system for high-density wireless neural implants for brain-machine interfaces. In IEEE 44th European Solid State Circuits Conference (ESSCIRC) 230–233 (IEEE, 2018).

  183. Seo, D., Carmena, J. M., Rabaey, J. M., Alon, E. & Maharbiz, M. M. Neural dust: an ultrasonic, low power solution for chronic brain–machine interfaces. Preprint at https://arxiv.org/abs/1307.2196 (2013).

  184. Lee, J. et al. An implantable wireless network of distributed microscale sensors for neural applications. In 9th International IEEE/EMBS Conference on Neural Engineering (NER) 871–874 (IEEE, 2019).

  185. Neely, R. M., Piech, D. K., Santacruz, S. R., Maharbiz, M. M. & Carmena, J. M. Recent advances in neural dust: towards a neural interface platform. Curr. Opin. Neurobiol. 50, 64–71 (2018).

    Article  CAS  PubMed  Google Scholar 

  186. Seo, D., Carmena, J. M., Rabaey, J. M., Maharbiz, M. M. & Alon, E. Model validation of untethered, ultrasonic neural dust motes for cortical recording. J. Neurosci. Methods 244, 114–122 (2015).

    Article  PubMed  Google Scholar 

  187. Piech, D. K. et al. A wireless millimetre-scale implantable neural stimulator with ultrasonically powered bidirectional communication. Nat. Biomed. Eng. 4, 207–222 (2020).

    Article  PubMed  Google Scholar 

  188. Seo, D. et al. Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91, 529–539 (2016).

    Article  CAS  PubMed  Google Scholar 

  189. Ghanbari, M. M. et al. 17.5 A 0.8mm3 ultrasonic implantable wireless neural recording system with linear AM backscattering. In IEEE International Solid-State Circuits Conference (ISSCC) 284–286 (IEEE, 2019).

  190. Charthad, J., Weber, M. J., Chang, T. C. & Arbabian, A. A mm-sized implantable medical device (IMD) with ultrasonic power transfer and a hybrid bi-directional data link. IEEE J. Solid State Circuits 50, 1741–1753 (2015).

    Article  Google Scholar 

  191. Charthad, J. et al. A mm-sized wireless implantable device for electrical stimulation of peripheral nerves. IEEE Trans. Biomed. Circuits Syst. 12, 257–270 (2018).

    Article  PubMed  Google Scholar 

  192. Shi, C., Costa, T., Elloian, J., Zhang, Y. & Shepard, K. A 0.065-mm3 monolithically-integrated ultrasonic wireless sensing mote for real-time physiological temperature monitoring. IEEE Trans. Biomed. Circuits Syst. 14, 412–424 (2020).

    Article  PubMed  Google Scholar 

  193. Phillips, W. B., Towe, B. C. & Larson, P. J. An ultrasonically-driven piezoelectric neural stimulator. In Proc. 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1983–1986 (IEEE, 2003).

  194. Weber, M. J. et al. A miniaturized single-transducer implantable pressure sensor with time-multiplexed ultrasonic data and power links. IEEE J. Solid State Circuits 53, 1089–1101 (2018).

    Article  Google Scholar 

  195. Larson, P. J. & Towe, B. C. Miniature ultrasonically powered wireless nerve cuff stimulator. In 5th International IEEE/EMBS Conference on Neural Engineering 265–268 (IEEE, 2011).

  196. Sonmezoglu, S. & Maharbiz, M. M. 34.4 A 4.5mm3 deep-tissue ultrasonic implantable luminescence oxygen sensor. In IEEE International Solid-State Circuits Conference (ISSCC) 454–456 (IEEE, 2020).

  197. Cortese, A. J. et al. Microscopic sensors using optical wireless integrated circuits. Proc. Natl Acad. Sci. USA 117, 9173–9179 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  198. Stocking, K. C., Vazquez, A. L. & Kozai, T. D. Y. Intracortical neural stimulation with untethered, ultrasmall carbon fiber electrodes mediated by the photoelectric effect. IEEE Trans. Biomed. Eng. 66, 2402–2412 (2019).

    Article  PubMed  Google Scholar 

  199. Abdo, A. et al. Floating light-activated microelectrical stimulators tested in the rat spinal cord. J. Neural Eng. 8, 056012 (2011).

    Article  PubMed Central  PubMed  Google Scholar 

  200. Singer, A. et al. Magnetoelectric materials for miniature, wireless neural stimulation at therapeutic frequencies. Neuron 107, 631–643 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  201. Deer, T. R. et al. The Neurostimulation Appropriateness Consensus Committee (NACC) safety guidelines for the reduction of severe neurological injury. Neuromodulation 20, 15–30 (2017).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  203. Couldwell, W. T. et al. Computer-aided design/computer-aided manufacturing skull base drill. Neurosurg. Focus 42, E6 (2017).

    Article  PubMed  Google Scholar 

  204. Sato, T., Suzuki, T. & Mabuchi, K. A new multi-electrode array design for chronic neural recording, with independent and automatic hydraulic positioning. J. Neurosci. Methods 160, 45–51 (2007).

    Article  CAS  PubMed  Google Scholar 

  205. Jackson, N. et al. Long-term neural recordings using MEMS based moveable microelectrodes in the brain. Front. Neuroeng. 3, 10 (2010).

    PubMed Central  PubMed  Google Scholar 

  206. Fee, M. S. & Leonardo, A. Miniature motorized microdrive and commutator system for chronic neural recording in small animals. J. Neurosci. Methods 112, 83–94 (2001).

    Article  CAS  PubMed  Google Scholar 

  207. Muthuswamy, J., Anand, S. & Sridharan, A. Adaptive movable neural interfaces for monitoring single neurons in the brain. Front. Neurosci. 5, 94 (2011).

    Article  PubMed Central  PubMed  Google Scholar 

  208. Zoll, R. S. et al. MEMS-actuated carbon fiber microelectrode for neural recording. IEEE Trans. Nanobiosci. 18, 234–239 (2019).

    Article  Google Scholar 

  209. Stieglitz, T. Why neurotechnologies? About the purposes, opportunities and limitations of neurotechnologies in clinical applications. Neuroethics 14, 5–16 (2019).

    Article  Google Scholar 

  210. Eaton, M. L. & Illes, J. Commercializing cognitive neurotechnology—the ethical terrain. Nat. Biotechnol. 25, 393–397 (2007).

    Article  CAS  PubMed  Google Scholar 

  211. Thakor, N. V. Translating the brain-machine interface. Sci. Transl. Med. 5, 210ps17 (2013).

    Article  PubMed  Google Scholar 

  212. Koch, J., Schuettler, M., Pasluosta, C. & Stieglitz, T. Electrical connectors for neural implants: design, state of the art and future challenges of an underestimated component. J. Neural Eng. 16, 061002 (2019).

    Article  PubMed  Google Scholar 

  213. Rose, T. L. & Robblee, L. S. Electrical stimulation with Pt electrodes. VIII. Electrochemically safe charge injection limits with 0.2 ms pulses (neuronal application). IEEE Trans. Biomed. Eng. 37, 1118–1120 (1990).

    Article  CAS  PubMed  Google Scholar 

  214. Negi, S., Bhandari, R., Rieth, L. & Solzbacher, F. In vitro comparison of sputtered iridium oxide and platinum-coated neural implantable microelectrode arrays. Biomed. Mater. 5, 015007 (2010).

    Article  CAS  Google Scholar 

  215. Weremfo, A., Carter, Hibbert, D. B. & Zhao, C. Investigating the interfacial properties of electrochemically roughened platinum electrodes for neural stimulation. Langmuir 31, 2593–2599 (2015).

    Article  CAS  PubMed  Google Scholar 

  216. Boehler, C., Oberueber, F., Schlabach, S., Stieglitz, T. & Asplund, M. Long-term stable adhesion for conducting polymers in biomedical applications: IrOx and nanostructured platinum solve the chronic challenge. ACS Appl. Mater. Interfaces 9, 189–197 (2017).

    Article  CAS  PubMed  Google Scholar 

  217. Weiland, J. D., Anderson, D. J. & Humayun, M. S. In vitro electrical properties for iridium oxide versus titanium nitride stimulating electrodes. IEEE Trans. Biomed. Eng. 49, 1574–1579 (2002).

    Article  PubMed  Google Scholar 

  218. Cogan, S. F., Troyk, R., Ehrlich, J., Plante, T. D. & Detlefsen, D. E. Potential-biased, asymmetric waveforms for charge-injection with activated iridium oxide (AIROF) neural stimulation electrodes. IEEE Trans. Biomed. Eng. 53, 327–332 (2006).

    Article  PubMed  Google Scholar 

  219. Ghazavi, A., Maeng, J., Black, M., Salvi, S. & Cogan, S. F. Electrochemical characteristics of ultramicro-dimensioned SIROF electrodes for neural stimulation and recording. J. Neural Eng. 17, 016022 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  220. Deku, F., Joshi-Imre, A., Mertiri, A., Gardner, T. J. & Cogan, S. F. Electrodeposited iridium oxide on carbon fiber ultramicroelectrodes for neural recording and stimulation. J. Electrochem. Soc. 165, D375 (2018).

    Article  CAS  Google Scholar 

  221. Zhou, D. M. & Greenberg, R. J. Electrochemical characterization of titanium nitride microelectrode arrays for charge-injection applications. In Proc. 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1964–1967 (IEEE, 2003).

  222. Deku, F. et al. Amorphous silicon carbide ultramicroelectrode arrays for neural stimulation and recording. J. Neural Eng. 15, 016007 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  223. Cui, X. T. & Zhou, D. D. Poly (3,4-ethylenedioxythiophene) for chronic neural stimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 502–508 (2007).

    Article  PubMed  Google Scholar 

  224. Jia, X. & Kohn, A. Gamma rhythms in the brain. PLoS Biol. 9, e1001045 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  225. Opie, N. L. et al. Chronic impedance spectroscopy of an endovascular stent-electrode array. J. Neural Eng. 13, 046020 (2016).

    Article  PubMed  Google Scholar 

  226. McAdams, E. T., Lackermeier, A., McLaughlin, J. A., Macken, D. & Jossinet, J. The linear and non-linear electrical properties of the electrode–electrolyte interface. Biosens. Bioelectron. 10, 67–74 (1995).

    Article  CAS  Google Scholar 

  227. Weiland, J. D. & Anderson, D. J. Chronic neural stimulation with thin-film, iridium oxide electrodes. IEEE Trans. Biomed. Eng. 47, 911–918 (2000).

    Article  CAS  PubMed  Google Scholar 

  228. Arcot Desai, S., Rolston, J. D., Guo, L. & Potter, S. M. Improving impedance of implantable microwire multi-electrode arrays by ultrasonic electroplating of durable platinum black. Front. Neuroeng. 3, 5 (2010).

    Google Scholar 

  229. Ludwig, K. A., Uram, J. D., Yang, J., Martin, D. C. & Kipke, D. R. Chronic neural recordings using silicon microelectrode arrays electrochemically deposited with a poly (3,4-ethylenedioxythiophene) (PEDOT) film. J. Neural Eng. 3, 59–70 (2006).

    Article  PubMed  Google Scholar 

  230. Mohit, A. A., Samii, A., Slimp, J. C., Grady, M. S. & Goodkin, R. Mechanical failure of the electrode wire in deep brain stimulation. Parkinsonism Relat. Disord. 10, 153–156 (2004).

    Article  Google Scholar 

  231. Sankar, V. et al. Electrode impedance analysis of chronic tungsten microwire neural implants: understanding abiotic vs. biotic contributions. Front. Neuroeng. 7, 13 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  232. Szarowski, D. H. et al. Brain responses to micro-machined silicon devices. Brain Res. 983, 23–35 (2003).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  234. Zhao, Z. et al. Ultraflexible electrode arrays for months-long high-density electrophysiological mapping of thousands of neurons in rodents. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00941-y (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  235. Viveros, R. D. et al. Advanced one- and two-dimensional mesh designs for injectable electronics. Nano Lett. 19, 4180–4187 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

O.C. was supported by a National Science Foundation Graduate Research Fellowship. J.L.E. was supported by a Hertz Fellowship. M.M.M. is a Chan-Zuckerberg Biohub Investigator.

Author information

Authors and Affiliations

Authors

Contributions

M.M.M. and K.S. supervised the project. K.S., O.C., J.L.E. and D.K.P. performed literature review and wrote the manuscript. K.S. prepared the figures, with contributions from O.C. All authors contributed to the revision of the manuscript.

Corresponding author

Correspondence to Konlin Shen.

Ethics declarations

Competing interests

M.M.M. is an employee of iota Biosciences, Inc., a fully owned subsidiary of Astellas Pharma., Inc. D.K.P. is bound by a confidentiality agreement to not disclose details of a potential competing interest. K.S., O.C. and J.L.E. declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Jacob Robinson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, K., Chen, O., Edmunds, J.L. et al. Translational opportunities and challenges of invasive electrodes for neural interfaces. Nat. Biomed. Eng 7, 424–442 (2023). https://doi.org/10.1038/s41551-023-01021-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-023-01021-5

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research