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:

Electronic neural interfaces

Nature Electronics volume 3, pages 191–200 (2020)Cite this article

Subjects

Abstract

Devices such as keyboards and touchscreens allow humans to communicate with machines. Neural interfaces, which can provide a direct, electrical bridge between analogue nervous systems and digital man-made systems, could provide a more efficient route to future information exchange. Here we review the development of electronic neural interfaces. The interfaces typically consist of three modules — a tissue interface, a sensing interface, and a neural signal processing unit — and based on technical milestones in the development of the electronic sensing interface, we group and analyse the interfaces in four generations: the patch clamp technique, multi-channel neural interfaces, implantable/wearable neural interfaces and integrated neural interfaces. We also consider key circuit and system challenges in the design of neural interfaces and explore the opportunities that arise with the latest technology

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: The development of neural interfaces.
Fig. 2: The electrophysiology behind the patch clamp technology.
Fig. 3: Typical neural signal characteristics and neural electrodes.
Fig. 4: Electrode–tissue interface model and stimulation waveform patterns.

Similar content being viewed by others

References

  1. Galvani, L. De Viribus Electricitatis In Motu Musculari Commentarius Bologna (Academy of Sciences, 1791).

  2. Marmont, G. Studies on the axon membrane. I. A new method. J. Cell. Compar. Physiol. 34, 351–382 (1949).

    Google Scholar 

  3. Hodgkin, A. L. & Huxley, A. F. Currents carried by sodium and potassium ions through the membrane of the giant axon of loligo. J. Physiol. 116, 449–472 (1952).

    Google Scholar 

  4. Hodgkin, A. L. & Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952).

    Google Scholar 

  5. Neher, E. & Sakmann, B. Single-channel currents recorded from membrane of denervated frog muscle fibres. Nature 260, 799–802 (1976).

    Google Scholar 

  6. Hamill, O. P., Marty, A., Neher, E., Sakmann, B. & Sigworth, F. Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflüg. Arch. 391, 85–100 (1981).

    Google Scholar 

  7. Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nat. Rev Neurosci. 13, 407–420 (2012).

    Google Scholar 

  8. Konerding, W., Froriep, U., Kral, A. & Baumhoff, P. New thin-film surface electrode array enables brain mapping with high spatial acuity in rodents. Sci. Rep. 8, 3825 (2018).

    Google Scholar 

  9. Lopez-Gordo, M., Sanchez-Morillo, D. & Valle, F. Dry EEG electrodes. Sensors 14, 12847–12870 (2014).

    Google Scholar 

  10. Xu, J., Mitra, S., Van Hoof, C., Yazicioglu, R. F. & Makinwa, K. A. Active electrodes for wearable EEG acquisition: review and electronics design methodology. IEEE Rev. Biomed. Eng. 10, 187–198 (2017).

    Google Scholar 

  11. Klem, G. H., Lüders, H. O., Jasper, H. & Elger, C. et al. The ten-twenty electrode system of the international federation. Electroen. Clin. Neurophysiol. 52, 3–6 (1999).

    Google Scholar 

  12. Jurcak, V., Tsuzuki, D. & Dan, I. 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34, 1600–1611 (2007).

    Google Scholar 

  13. Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G. & Moran, D. W. A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63–71 (2004).

    Google Scholar 

  14. Tolstosheeva, E. et al. A multi-channel, flex-rigid ECoG microelectrode array for visual cortical interfacing. Sensors 15, 832–854 (2015).

    Google Scholar 

  15. Blau, A. et al. Flexible, all-polymer microelectrode arrays for the capture of cardiac and neuronal signals. Biomaterials 32, 1778–1786 (2011).

    Google Scholar 

  16. Khodagholy, D. et al. In vivo recordings of brain activity using organic transistors. Nat. Commun. 4, 1575–1575 (2013).

    Google Scholar 

  17. Escabí, M. A. et al. A high-density, high-channel count, multiplexed µECoG array for auditory-cortex recordings. J. Neurophysiol. 112, 1566–1583 (2014).

    Google Scholar 

  18. Lehew, G. & Nicolelis, M. A. in Methods for Neural Ensemble Recordings 2nd edn (ed. Nicolelis, M. A.) Ch. 1 (Taylor & Francis, 2008).

  19. Nicolelis, M. A. et al. Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl Acad. Sci. USA 100, 11041–11046 (2003).

    Google Scholar 

  20. Buzsáki, G. Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, 446–451 (2004).

    Google Scholar 

  21. Obaid, A. M. et al. Massively parallel microwire arrays integrated with CMOS chips for neural recording. Preprint at https://www.biorxiv.org/content/10.1101/573295v1 (2019).

  22. Wise, K. D., Anderson, D., Hetke, J., Kipke, D. & Najafi, K. Wireless implantable microsystems: high-density electronic interfaces to the nervous system. Proc. IEEE 92, 76–97 (2004).

    Google Scholar 

  23. Rousche, P. J. & Normann, R. A. Chronic recording capability of the Utah intracortical electrode array in cat sensory cortex. J. Neurosci. Meth. 82, 1–15 (1998).

    Google Scholar 

  24. Abidian, M. R. & Martin, D. C. Multifunctional nanobiomaterials for neural interfaces. Adv. Func. Mater. 19, 573–585 (2009).

    Google Scholar 

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

    Google Scholar 

  26. 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 https://www.biorxiv.org/content/10.1101/578542v1 (2019).

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

    Google Scholar 

  28. 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).

    Google Scholar 

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

    Google Scholar 

  30. Lawrence, S. M., Dhillon, G. S. & Horch, K. W. Fabrication and characteristics of an implantable, polymer-based, intrafascicular electrode. J.Nneurosci. Meth. 131, 9–26 (2003).

    Google Scholar 

  31. Boretius, T. et al. A transverse intrafascicular multichannel electrode (TIME) to interface with the peripheral nerve. Biosens. Bioelectron. 26, 62–69 (2010).

    Google Scholar 

  32. Raspopovic, S. et al. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci. Transl. Med. 6, 222ra19–222ra19 (2014).

    Google Scholar 

  33. Akin, T., Najafi, K., Smoke, R. H. & Bradley, R. M. A micromachined silicon sieve electrode for nerve regeneration applications. IEEE Trans. Biomed. Eng. 41, 305–313 (1994).

    Google Scholar 

  34. Nerve Cuff Electrodes. MicroProbes for Life Science (2020); https://microprobes.com/products/peripheral-electrodes/nerve-cuff

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

    Google Scholar 

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

    Google Scholar 

  37. Van Dongen, M. & Serdijn, W. Design of Efficient and Safe Neural Stimulators (Springer, 2016).

  38. Paulus, W. Transcranial electrical stimulation (tES–tDCS; tRNS, tACS) methods. Neuropsychol. Rehabil. 21, 602–617 (2011).

    Google Scholar 

  39. Cameron, T. Safety and efficacy of spinal cord stimulation for the treatment of chronic pain: a 20-year literature review. J. Neurosurg. Spine 100, 254–267 (2004).

    Google Scholar 

  40. Kringelbach, M. L., Jenkinson, N., Owen, S. L. & Aziz, T. Z. Translational principles of deep brain stimulation. Nat. Rev. Neurosci. 8, 623–635 (2007).

    Google Scholar 

  41. Groves, D. A. & Brown, V. J. Vagal nerve stimulation: a review of its applications and potential mechanisms that mediate its clinical effects. Neurosci. Biobehav. Rev. 29, 493–500 (2005).

    Google Scholar 

  42. Hallett, M. Transcranial magnetic stimulation: a primer. Neuron 55, 187–199 (2007).

    Google Scholar 

  43. Luo, D., Zhang, M. & Wang, Z. A low-noise chopper amplifier designed for multi-channel neural signal acquisition. IEEE J. Solid-State Circ. 54, 2255–2265 (2019).

    Google Scholar 

  44. Yaul, F. M. & Chandrakasan, A. P. A sub-µW 36nV/√Hz chopper amplifier for sensors using a noise-efficient inverter-based 0.2V-supply input stage. In IEEE Int. Solid-State Circuits Conference (ISSCC) 94–95 (IEEE, 2016).

  45. Johnson, B. C. et al. An implantable 700µW 64-channel neuromodulation IC for simultaneous recording and stimulation with rapid artifact recovery. In 2017 Symposium on VLSI Circuits C48–C49 (IEEE, 2017).

  46. Kim, C. et al. A 92dB dynamic range sub-μV rms-noise 0.8µW/ch neural-recording ADC array with predictive digital autoranging. In 2018 IEEE Int. Solid-State Circuits Conference (ISSCC) 470–472 (IEEE, 2018).

  47. Chandrakumar, H. & Markovic, D. A 15.2-ENOB continuous-time ΔΣ ADC for a 7.3 µW 200 mV pp-linear-input-range neural recording front-end. In 2018 IEEE Int. Solid-State Circuits Conference (ISSCC) 232–234 (IEEE, 2018).

  48. Rozgić, D. et al. A 0.338cm3, artifact-free, 64-contact neuromodulation platform for simultaneous stimulation and sensing. IEEE Trans. Biomed. Circ. Syst. 13, 38–55 (2018).

    Google Scholar 

  49. Ha, U. et al. An EEG-NIRS multimodal SoC for accurate anesthesia depth monitoring. IEEE J. Solid-State Circ. 53, 1830–1843 (2018).

    Google Scholar 

  50. Lee, S. et al. A 110dB-CMRR 100dB-PSRR multi-channel neural-recording amplifier system using differentially regulated rejection ratio enhancement in 0.18 µm CMOS. In 2018 IEEE Int. Solid-State Circuits Conference (ISSCC) 472–474 (IEEE, 2018).

  51. Harrison, R. R. & Charles, C. A low-power low-noise CMOS amplifier for neural recording applications. IEEE J. Solid-State Circ. 38, 958–965 (2003).

    Google Scholar 

  52. Zhao, Y., Shang, Z. & Lian, Y. A 2.55 NEF 76 dB CMRR DC-coupled fully differential difference amplifier based analog front end for wearable biomedical sensors. IEEE Trans. Biomed. Circ. Syst. 13, 918–926 (2019).

    Google Scholar 

  53. Lee, B. & Ghovanloo, M. An adaptive averaging low noise front-end for central and peripheral nerve recording. IEEE Transactions on Circuits and Systems II: Express Briefs 65, 839–843 (2017).

    Google Scholar 

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

    Google Scholar 

  55. Kassiri, H. et al. 27.3 All-wireless 64-channel 0.013mm 2/ch closed-loop neurostimulator with rail-to-rail DC offset removal. In 2017 IEEE Int. Solid-State Circuits Conference (ISSCC) 452–453 (IEEE, 2017).

  56. O’Leary, G. et al. A recursive-memory brain-state classifier with 32-channel track-and-zoom Δ2Σ ADCs and charge-balanced programmable waveform neurostimulators. In 2018 IEEE Int. Solid-State Circuits Conference (ISSCC) 296–298 (IEEE, 2018).

  57. Jeon, H., Bang, J.-S., Jung, Y., Choi, I. & Je, M. A high DR, DC-coupled, time-based neural-recording IC with degeneration R-DAC for bidirectional neural interface. IEEE J. Solid-State Circ. 54, 2658–2670 (2019).

    Google Scholar 

  58. Leene, L. B. & Constandinou, T. G. A 0.006mm2 1.2µW analog-to-time converter for asynchronous bio-sensors. IEEE J. Solid-State Circ. 53, 2604–2613 (2018).

    Google Scholar 

  59. Haas, M. & Ortmanns, M. Efficient implementation and stability analysis of a HV-CMOS current/voltage mode stimulator. In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) https://doi.org/10.1109/BIOCAS.2018.8584804 (IEEE, 2018).

  60. Butz, N., Taschwer, A., Manoli, Y. & Kuhl, M. 22.6 A 22V compliant 56µW active charge balancer enabling 100% charge compensation even in monophasic and 36% amplitude correction in biphasic neural stimulators. In 2016 IEEE Int. Solid-State Circuits Conference (ISSCC) 390–391 (IEEE, 2016).

  61. Greenwald, E. et al. A CMOS current steering neurostimulation array with integrated DAC calibration and charge balancing. IEEE transactions on biomedical circuits and systems 11, 324–335 (2017).

    MathSciNet  Google Scholar 

  62. Lee, H.-M., Kwon, K. Y., Li, W. & Ghovanloo, M. A power-efficient switched-capacitor stimulating system for electrical/optical deep brain stimulation. IEEE J. Solid-State Circ. 50, 360–374 (2014).

    Google Scholar 

  63. Sooksood, K., Stieglitz, T. & Ortmanns, M. An active approach for charge balancing in functional electrical stimulation. IEEE Transactions on Biomedical Circuits and Systems 4, 162–170 (2010).

    Google Scholar 

  64. Kwong, J. & Chandrakasan, A. P. An energy-efficient biomedical signal processing platform. IEEE J. Solid-State Circ. 46, 1742–1753 (2011).

    Google Scholar 

  65. Cong, P. et al. A 32-channel modular bi-directional neural interface system with embedded DSP for closed-loop operation. In European Solid State Circuits Conference (ESSCIRC) 99–102 (IEEE, 2014).

  66. Alzuhair, A. & Marković, D. A 216 nW/channel DSP engine for triggering theta phase-locked brain stimulation. In 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) https://doi.org/10.1109/BIOCAS.2017.8325189 (IEEE, 2017).

  67. Rhew, H.-G. et al. A fully self-contained logarithmic closed-loop deep brain stimulation SoC with wireless telemetry and wireless power management. IEEE J. Solid-State Circ. 49, 2213–2227 (2014).

    Google Scholar 

  68. Altaf, M. A. B., Zhang, C. & Yoo, J. A 16-channel patient-specific seizure onset and termination detection SoC with impedance-adaptive transcranial electrical stimulator. IEEE J. Solid-State Circ. 50, 2728–2740 (2015).

    Google Scholar 

  69. Azin, M., Guggenmos, D. J., Barbay, S., Nudo, R. J. & Mohseni, P. A battery-powered activity-dependent intracortical microstimulation IC for brain-machine-brain interface. IEEE J. Solid-State Circ. 46, 731–745 (2011).

    Google Scholar 

  70. Iranmanesh, S. & Rodriguez-Villegas, E. A 950 nW analog-based data reduction chip for wearable EEG systems in epilepsy. IEEE J. Solid-State Circ. 52, 2362–2373 (2017).

    Google Scholar 

  71. Cheng, C.-H. et al. A fully integrated 16-channel closed-loop neural-prosthetic CMOS SoC with wireless power and bidirectional data telemetry for real-time efficient human epileptic seizure control. IEEE J. Solid-State Circ. 53, 3314–3326 (2018).

    Google Scholar 

  72. Pazhouhandeh, M. R. et al. 22.8 Adaptively clock-boosted auto-ranging responsive neurostimulator for emerging neuromodulation applications. In 2019 IEEE Int. Solid-State Circuits Conference (ISSCC), 374–376 (IEEE, 2019).

  73. Imtiaz, S. A., Jiang, Z. & Rodriguez-Villegas, E. An ultralow power system on chip for automatic sleep staging. IEEE J. Solid-State Circ. 52, 822–833 (2017).

    Google Scholar 

  74. Chang, S.-Y. et al. An ultra-low-power dual-mode automatic sleep staging processor using neural-network-based decision tree. IEEE Trans. Circ. Syst. I 66, 3504–3516 (2019).

    Google Scholar 

  75. Chen, Y., Yao, E. & Basu, A. A 128-channel extreme learning machine-based neural decoder for brain machine interfaces. IEEE Trans. Biomed. Circ. Syst. 10, 679–692 (2016).

    Google Scholar 

  76. Harrison, R. R. The design of integrated circuits to observe brain activity. Proc. IEEE 96, 1203–1216 (2008).

    Google Scholar 

  77. Zhang, F., Mishra, A., Richardson, A. G. & Otis, B. A low-power ECoG/EEG processing IC with integrated multiband energy extractor. IEEE Trans. Circ. Syst. I 58, 2069–2082 (2011).

    MathSciNet  Google Scholar 

  78. Karkare, V., Gibson, S. & Markovic, D. A 130-µW, 64-channel neural spike-sorting DSP chip. IEEE J. Solid-State Circ. 46, 1214–1222 (2011).

    Google Scholar 

  79. Wu, T. et al. A 16-channel nonparametric spike detection ASIC based on EC-PC decomposition. IEEE Trans. Biomed. Circ. Syst. 10, 3–17 (2016).

    Google Scholar 

  80. Chen, T.-C., Chen, K., Yang, Z., Cockerham, K. & Liu, W. A biomedical multiprocessor SoC for closed-loop neuroprosthetic applications. In IEEE Int. Solid-State Circuits Conference - Digest of Technical Papers 434–435 (IEEE, 2009).

  81. Karkare, V., Gibson, S. & Marković, D. A 75-µW, 16-channel neural spike-sorting processor with unsupervised clustering. IEEE J. Solid-State Circ. 48, 2230–2238 (2013).

    Google Scholar 

  82. Do, A. T., Zeinolabedin, S. M. A., Jeon, D., Sylvester, D. & Kim, T. T.-H. An area-efficient 128-channel spike sorting processor for real-time neural recording with 0.175μW/channel in 65-nm CMOS. IEEE Trans. VLSI Syst. 27, 126–137 (2018).

    Google Scholar 

  83. Aprile, C. et al. Adaptive learning-based compressive sampling for low-power wireless implants. IEEE Trans. Circ. Syst. I 65, 3929–3941 (2018).

    Google Scholar 

  84. Ranjandish, R. & Schmid, A. A sub-µW/channel, 16-channel seizure detection and signal acquisition SoC based on multichannel compressive sensing. IEEE Trans. Circ. Syst. II 65, 1400–1404 (2018).

    Google Scholar 

  85. Liu, X. et al. A fully integrated wireless compressed sensing neural signal acquisition system for chronic recording and brain machine interface. IEEE Trans. Biomed. Circ. Syst. 10, 874–883 (2016).

    Google Scholar 

  86. Biederman, W. et al. A 4.78mm2 fully-integrated neuromodulation SoC combining 64 acquisition channels with digital compression and simultaneous dual stimulation. IEEE J. Solid-State Circ. 50, 1038–1047 (2015).

    Google Scholar 

  87. Kim, S.-J. et al. A sub-µW/Ch analog front-end for Δ-neural recording with spike-driven data compression. IEEE Trans. Biomed. Circ. Syst. https://doi.org/10.1109/TBCAS.2018.2880257 (2018).

  88. Liu, X., Zhang, M., Richardson, A. G., Lucas, T. H. & Van der Spiegel, J. Design of a closed-loop, bidirectional brain machine interface system with energy efficient neural feature extraction and PID control. IEEE Trans. Biomed. Circ. Syst. 11, 729–742 (2017).

    Google Scholar 

  89. Venkatraman, S., Elkabany, K., Long, J. D., Yao, Y. & Carmena, J. M. A system for neural recording and closed-loop intracortical microstimulation in awake rodents. IEEE Trans. Biomed. Eng. 56, 15–22 (2009).

    Google Scholar 

  90. Mendrela, A. E. et al. A bidirectional neural interface circuit with active stimulation artifact cancellation and cross-channel common-mode noise suppression. IEEE J. Solid-State Circ. 51, 955–965 (2016).

    Google Scholar 

  91. Zhou, A., Johnson, B. C. & Muller, R. Toward true closed-loop neuromodulation: artifact-free recording during stimulation. Curr. Opin. Neurobiol. 50, 119–127 (2018).

    Google Scholar 

  92. Stanslaski, S. et al. Creating neural “co-processors” to explore treatments for neurological disorders. In IEEE Int. Solid-State Circuits Conference (ISSCC) 460–462 (IEEE, 2018).

  93. Thomas, G. P. & Jobst, B. C. Critical review of the responsive neurostimulator system for epilepsy. Med. Dev. 8, 405 (2015).

    Google Scholar 

  94. Kohler, F. et al. Closed-loop interaction with the cerebral cortex: a review of wireless implant technology. Brain-Comp. Interf. 4, 146–154 (2017).

    Google Scholar 

  95. Xu, J. et al. A 160μW 8-channel active electrode system for EEG monitoring. IEEE Trans. Biomed. Circ. Syst. 5, 555–567 (2011).

    Google Scholar 

  96. Xu, J. et al. A 665µW silicon photomultiplier-based NIRS/EEG/EIT monitoring ASIC for wearable functional brain imaging. In IEEE Int. Solid-State Circuits Conference (ISSCC) 294–296 (IEEE, 2018).

  97. Pfurtscheller, G. et al. The hybrid BCI. Front. Neurosci. 4, 30 (2010).

    Google Scholar 

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

    Google Scholar 

  99. Angotzi, G. N., Boi, F., Zordan, S., Bonfanti, A. & Vato, A. A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals. Sci. Rep. 4, 5963 (2014).

    Google Scholar 

  100. Yin, M. et al. Wireless neurosensor for full-spectrum electrophysiology recordings during free behavior. Neuron 84, 1170–1182 (2014).

    Google Scholar 

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

    Google Scholar 

  102. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. Brain-machine interface: Instant neural control of a movement signal. Nature 416, 141–142 (2002).

    MATH  Google Scholar 

  103. Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

    Google Scholar 

  104. Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl Acad. Sci. USA 101, 17849–17854 (2004).

    Google Scholar 

  105. Rebsamen, B. et al. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 590–598 (2010).

    Google Scholar 

  106. Chaudhary, U., Birbaumer, N. & Ramos-Murguialday, A. Brain–computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 12, 513–525 (2016).

    Google Scholar 

  107. Tran, N. et al. A complete 256-electrode retinal prosthesis chip. IEEE J. Solid-State Circ. 49, 751–765 (2014).

    Google Scholar 

  108. Ramos-Murguialday, A. et al. Brain–machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108 (2013).

    Google Scholar 

  109. Capogrosso, M. et al. A brain–spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539, 284–288 (2016).

    Google Scholar 

  110. Wagner, F. B. et al. Targeted neurotechnology restores walking in humans with spinal cord injury. Nature 563, 65–71 (2018).

    Google Scholar 

  111. Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016).

    Google Scholar 

  112. Tabot, G. A. et al. Restoring the sense of touch with a prosthetic hand through a brain interface. Proc. Natl Acad. Sci. USA 18279–18284 (2013).

  113. Lopez, C. M. et al. 22.7 A 966-electrode neural probe with 384 configurable channels in 0.13 µm SOI CMOS. In 2016 IEEE Int. Solid-State Circuits Conference (ISSCC) 392–393 (IEEE, 2016).

  114. Herbawi, A. S., Kießner, L., Paul, O. & Rüther, P. High-density CMOS neural probe implementing a hierarchical addressing scheme for 1600 recording sites and 32 output channels. In 19th Int. Conf. Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS) 20–23 (IEEE, 2017).

  115. De Dorigo, D. et al. A fully immersible deep-brain neural probe with modular architecture and a delta-sigma ADC integrated under each electrode for parallel readout of 144 recording sites. In IEEE Int. Solid-State Circuits Conference (ISSCC) 462–464 (IEEE, 2018).

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

    Google Scholar 

  117. Biederman, W. et al. A fully-integrated, miniaturized (0.125 mm2) 10.5 µW wireless neural sensor. IEEE J. Solid-State Circ. 48, 960–970 (2013).

    Google Scholar 

  118. Kim, C. et al. A 3 mm× 3 mm fully integrated wireless power receiver and neural interface system-on-chip. IEEE Trans. Biomed. Circ. Syst. 13, 1736–1746 (2019).

    Google Scholar 

  119. Yeon, P., Bakir, M. S. & Ghovanloo, M. Towards a 1.1 mm2 free-floating wireless implantable neural recording SoC. In 2018 IEEE Custom Integrated Circuits Conference (CICC) https://doi.org/10.1109/CICC.2018.8357048 (IEEE, 2018).

  120. Khalifa, A. et al. The microbead: A 0.009mm3 implantable wireless neural stimulator. IEEE Trans. Biomed. Circ. Syst. 13, 971–985 (2019).

    Google Scholar 

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

  122. Jia, Y. et al. A mm-sized free-floating wirelessly powered implantable optical stimulating system-on-a-chip. In IEEE Int. Solid-State Circuits Conference-(ISSCC) 468–470 (IEEE, 2018).

  123. Ghanbari, M. M. et al. 17.5 A 0.8 mm 3 ultrasonic implantable wireless neural recording system with linear AM backscattering. In 2019 IEEE Int. Solid-State Circuits Conference (ISSCC) 284–286 (IEEE, 2019).

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

    Google Scholar 

  125. 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).

    Google Scholar 

  126. Leene, L. B. et al. Autonomous SoC for neural local field potential recording in mm-scale wireless implants. In 2018 IEEE Int. Symp. Circuits and Systems (ISCAS) https://doi.org/10.1109/ISCAS.2018.8351147 (IEEE, 2018).

  127. Laiwalla, F. et al. A distributed wireless network of implantable sub-mm cortical microstimulators for brain-computer interfaces. In 2019 41st Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC) 6876–6879 (IEEE, 2019).

  128. Naor, O., Krupa, S. & Shoham, S. Ultrasonic neuromodulation. J. Neural Eng. 13, 031003 (2016).

    Google Scholar 

  129. Gagnon-Turcotte, G., Ethier, C., De Köninck, Y. & Gosselin, B. A 13µm CMOS SoC for simultaneous multichannel optogenetics and electrophysiological brain recording. In 2018 IEEE Int. Solid-State Circuits Conference (ISSCC) 466–468 (IEEE, 2018).

  130. Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).

    Google Scholar 

  131. Deisseroth, K. Optogenetics: 10 years of microbial opsins in neuroscience. Nat. Neurosci. 18, 1213–1225 (2015).

    Google Scholar 

  132. Ding, H. et al. Microscale optoelectronic infrared-to-visible upconversion devices and their use as injectable light sources. Proc. Natl Acad. Sci. USA 115, 6632–6637 (2018).

    Google Scholar 

  133. Anikeeva, P. et al. Optetrode: a multichannel readout for optogenetic control in freely moving mice. Nat. Neurosci. 15, 163–170 (2012).

    Google Scholar 

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

    Google Scholar 

  135. Hodgkin, A. Evidence for electrical transmission in nerve. J. Physiol. 90, 183–210 (1937).

    Google Scholar 

Download references

Acknowledgements

This work is supported in part by the Beijing Innovation Center for Future chip, in part by the Beijing National Research Center for Information Science and Technology, in part by the Natural Science Foundation of China through grant 61674095.

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Milin Zhang & Zijian Tang

  2. Department of Electrical and Systems Engineering (ESE), University of Pennsylvania, Philadelphia, PA, USA

    Xilin Liu & Jan Van der Spiegel

Authors
  1. Milin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zijian Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xilin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jan Van der Spiegel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.Z. and J.V.d.S. conceived the work and suggested the outline of the paper. M.Z. and X.L. worked on the study of various neural interface designs. Z.T. and M.Z. carried out investigations and wrote the paper.

Corresponding author

Correspondence to Milin Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, M., Tang, Z., Liu, X. et al. Electronic neural interfaces. Nat Electron 3, 191–200 (2020). https://doi.org/10.1038/s41928-020-0390-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-020-0390-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing