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Recent advances in wireless epicortical and intracortical neuronal recording systems

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  • Special Focus on Brain Machine Interfaces and Applications
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Abstract

An implantable brain-computer interface (BCI) has proven to be effective in the field of sensory and motor function restoration and in the treatment of neurological disorders. Using a BCI recording system, we can transform current methods of human interaction with machines and the environment, especially to help those with cognitive and mobility disabilities regain mobility and reintegrate into society. However, most reported work has focused on a simple aspect of the whole system, such as electrodes, circuits, or data transmission, and only a very small percentage of systems are wireless. A miniature, lightweight, wireless, implantable microsystem is key to realizing long-term, real-time, and stable monitoring on freely moving animals or humans in their natural conditions. Here, we summarize typical wireless recording systems, from recording electrodes, processing chips and controllers, wireless data transmission, and power supply to the system-level package for either epicortical electrocorticogram (ECoG) or intracortical local field potentials (LFPs)/spike acquisition, developed in recent years. Finally, we conclude with our vision of challenges in next-generation wireless neuronal recording systems for chronic and safe applications.

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References

  1. Homer M L, Nurmikko A V, Donoghue J P, et al. Sensors and decoding for intracortical brain computer interfaces. Annu Rev Biomed Eng, 2013, 15: 383–405

    Article  Google Scholar 

  2. Brandman D M, Cash S S, Hochberg L R. Human intracortical recording and neural decoding for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2017, 25: 1687–1696

    Article  Google Scholar 

  3. Szostak K M, Grand L, Constandinou T G. Neural interfaces for intracortical recording: requirements, fabrication methods, and characteristics. Front Neurosci, 2017, 11: 665

    Article  Google Scholar 

  4. Miller K J, Hermes D, Staff N P. The current state of electrocorticography-based brain-computer interfaces. NeuroSurg Focus, 2020, 49: 2

    Article  Google Scholar 

  5. Sharma K, Sharma R. Design considerations for effective neural signal sensing and amplification: a review. Biomed Phys Eng Express, 2019, 5: 042001

    Article  Google Scholar 

  6. Vansteensel M J, Pels E G M, Bleichner M G, et al. Fully implanted brain-computer interface in a locked-in patient with ALS. N Engl J Med, 2016, 375: 2060–2066

    Article  Google Scholar 

  7. Moses D A, Metzger S L, Liu J R, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N Engl J Med, 2021, 385: 217–227

    Article  Google Scholar 

  8. Hochberg L R, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 2012, 485: 372–375

    Article  Google Scholar 

  9. Flesher S N, Downey J E, Weiss J M, et al. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science, 2021, 372: 831–836

    Article  Google Scholar 

  10. Rajangam S, Tseng P H, Yin A, et al. Wireless cortical brain-machine interface for whole-body navigation in primates. Sci Rep-Uk, 2016, 6: 1–13

    Google Scholar 

  11. Libedinsky C, So R, Xu Z M, et al. Independent mobility achieved through a wireless brain-machine interface. PLoS ONE, 2016, 11: 0165773

    Article  Google Scholar 

  12. Benabid A L, Costecalde T, Eliseyev A, et al. An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol, 2019, 18: 1112–1122

    Article  Google Scholar 

  13. Bouton C E, Shaikhouni A, Annetta N V, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature, 2016, 533: 247–250

    Article  Google Scholar 

  14. Ganzer P D, Colachis S C, Schwemmer M A, et al. Restoring the sense of touch using a sensorimotor demultiplexing neural interface. Cell, 2020, 181: 763–773

    Article  Google Scholar 

  15. Maharbiz M M, Muller R, Alon E, et al. Reliable next-generation cortical interfaces for chronic brain-machine interfaces and neuroscience. Proc IEEE, 2017, 105: 73–82

    Article  Google Scholar 

  16. Yin M, Borton D A, Aceros J, et al. A 100-channel hermetically sealed implantable device for chronic wireless neurosensing applications. IEEE Trans Biomed Circ Syst, 2013, 7: 115–128

    Article  Google Scholar 

  17. Young C P, Liang S F, Chang D W, et al. A portable wireless online closed-loop seizure controller in freely moving rats. IEEE Trans Instrum Meas, 2011, 60: 513–521

    Article  Google Scholar 

  18. Zhou A, Santacruz S R, Johnson B C, et al. A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates. Nat Biomed Eng, 2019, 3: 15–26

    Article  Google Scholar 

  19. Fernandez-Leon J A, Parajuli A, Franklin R, et al. A wireless transmission neural interface system for unconstrained nonhuman primates. J Neural Eng, 2015, 12: 056005

    Article  Google Scholar 

  20. Wentz C T, Bernstein J G, Monahan P, et al. A wirelessly powered and controlled device for optical neural control of freely-behaving animals. J Neural Eng, 2011, 8: 046021

    Article  Google Scholar 

  21. Chang C W, Chiou J C. A wireless and batteryless microsystem with implantable grid electrode/3-dimensional probe array for ECoG and extracellular neural recording in rats. Sensors, 2013, 13: 4624–4639

    Article  Google Scholar 

  22. Matsushita K, Hirata M, Suzuki T, et al. A fully implantable wireless ECoG 128-channel recording device for human brain-machine interfaces: W-HERBS. Front Neurosci, 2018, 12: 511

    Article  Google Scholar 

  23. Lee B, Jia Y, Mirbozorgi S A, et al. An inductively-powered wireless neural recording and stimulation system for freely-behaving animals. IEEE Trans Biomed Circ Syst, 2019, 13: 413–424

    Article  Google Scholar 

  24. Keramatzadeh K, Kiakojouri A, Nahvi M S, et al. Wireless, miniaturized, semi-implantable electrocorticography microsystem validated in vivo. Sci Rep-Uk, 2020, 10: 1–13

    Google Scholar 

  25. Sauter-Starace F, Ratel D, Cretallaz C, et al. Long-term sheep implantation of WIMAGINE, a wireless 64-channel electrocorticogram recorder. Front Neurosci, 2019, 13: 847

    Article  Google Scholar 

  26. Borton D A, Yin M, Aceros J, et al. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J Neural Eng, 2013, 10: 026010

    Article  Google Scholar 

  27. Schwarz D A, Lebedev M A, Hanson T L, et al. Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nat Methods, 2014, 11: 670–676

    Article  Google Scholar 

  28. Simeral J D, Hosman T, Saab J, et al. Home use of a percutaneous wireless intracortical brain-computer interface by individuals with tetraplegia. IEEE Trans Biomed Eng, 2021, 68: 2313–2325

    Article  Google Scholar 

  29. Seo D, Carmena J M, Rabaey J M, et al. Model validation of untethered, ultrasonic neural dust motes for cortical recording. J Neurosci Methods, 2015, 244: 114–122

    Article  Google Scholar 

  30. Park S Y, Kyounghwan N, Voroslakos M, et al. A miniaturized 256-channel neural recording interface with area-efficient hybrid integration of flexible probes and CMOS integrated circuits. IEEE Tran Bio-Med Eng, 2021. doi: https://doi.org/10.1109/TBME.2021.3093542

  31. Liu X L, Zhang M L, Xiong T, et al. A fully integrated wireless compressed sensing neural signal acquisition system for chronic recording and brain machine interface. IEEE Trans Biomed Circ Syst, 2016, 10: 874–883

    Article  Google Scholar 

  32. Musk E. An integrated brain-machine interface platform with thousands of channels. J Med Int Res, 2019, 21: 16194

    Google Scholar 

  33. Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593: 249–254

    Article  Google Scholar 

  34. Silversmith D B, Abiri R, Hardy N F, et al. Plug-and-play control of a brain-computer interface through neural map stabilization. Nat Biotechnol, 2021, 39: 326–335

    Article  Google Scholar 

  35. Makin J G, Moses D A, Chang E F. Machine translation of cortical activity to text with an encoder-decoder framework. Nat Neurosci, 2020, 23: 575–582

    Article  Google Scholar 

  36. Sung C, Jeon W, Nam K S, et al. Multimaterial and multifunctional neural interfaces: from surface-type and implantable electrodes to fiber-based devices. J Mater Chem B, 2020, 8: 6624–6666

    Article  Google Scholar 

  37. Zhou Y H, Ji B W, Wang M H, et al. Implantable thin film devices as brain-computer interfaces: recent advances in design and fabrication approaches. Coatings, 2021, 11: 204

    Article  Google Scholar 

  38. Ha S, Akinin A, Park J, et al. Silicon-integrated high-density electrocortical interfaces. Proc IEEE, 2017, 105: 11–33

    Article  Google Scholar 

  39. Xing D J, Yeh C I, Shapley R M. Spatial spread of the local field potential and its laminar variation in visual cortex. J Neurosci, 2009, 29: 11540–11549

    Article  Google Scholar 

  40. Buzséki G, Anastassiou C A, Koch C. The origin of extracellular fields and currents-EEG, ECoG, LFP and spikes. Nat Rev Neurosci, 2012, 13: 407–420

    Article  Google Scholar 

  41. Fallegger F, Schiavone G, Pirondini E, et al. MRI-compatible and conformal electrocorticography grids for translational research. Adv Sci, 2021, 8: 2003761

    Article  Google Scholar 

  42. Renz A F, Lee J, Tybrandt K, et al. Opto-E-Dura: a soft, stretchable ECoG array for multimodal, multiscale neuroscience. Adv Healthc Mater, 2020, 9: 2000814

    Article  Google Scholar 

  43. Kaiju T, Inoue M, Hirata M, et al. High-density mapping of primate digit representations with a 1152-channel μECoG array. J Neural Eng, 2021, 18: 036025

    Article  Google Scholar 

  44. Shandhi M M H, Negi S. Fabrication of out-of-plane high channel density microelectrode neural array with 3D recording and stimulation capabilities. J Microelectromech Syst, 2020, 29: 522–531

    Article  Google Scholar 

  45. Sahasrabuddhe K, Khan A A, Singh A P, et al. The Argo: a high channel count recording system for neural recording in vivo. J Neural Eng, 2020, 18: 015002

    Google Scholar 

  46. Kollo M, Racz R, Hanna M E, et al. CHIME: CMOS-hosted in vivo microelectrodes for massively scalable neuronal recordings. Front Neurosci, 2020, 14: 834

    Article  Google Scholar 

  47. Steinmetz N A, Koch C, Harris K D, et al. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr Opin NeuroBiol, 2018, 50: 92–100

    Article  Google Scholar 

  48. Steinmetz N A, Aydin C, Lebedeva A, et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science, 2021, 372: eabf4588

    Article  Google Scholar 

  49. Guan S, Wang J, Gu X W, et al. Elastocapillary self-assembled neurotassels for stable neural activity recordings. Sci Adv, 2019, 5: 2842

    Article  Google Scholar 

  50. Ji B W, Ge C F, Guo Z J, et al. Flexible and stretchable opto-electric neural interface for low-noise electrocorticogram recordings and neuromodulation in vivo. Biosens Bioelectron, 2020, 153: 112009

    Article  Google Scholar 

  51. Dong R H, Wang L L, Hang C, et al. Printed stretchable liquid metal electrode arrays for in vivo neural recording. Small, 2021, 17: 2006612

    Article  Google Scholar 

  52. Seo J W, Kim K, Seo K W, et al. Artifact-free 2D mapping of neural activity in vivo through transparent gold nanonetwork array. Adv Funct Mater, 2020, 30: 2000896

    Article  Google Scholar 

  53. Qiang Y, Artoni P, Seo K J, et al. Transparent arrays of bilayer-nanomesh microelectrodes for simultaneous electrophysiology and two-photon imaging in the brain. Sci Adv, 2018, 4: 0626

    Article  Google Scholar 

  54. Viventi J, Kim D H, Vigeland L, et al. Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nat Neurosci, 2011, 14: 1599–1605

    Article  Google Scholar 

  55. Schaefer N, Garcia-Cortadella R, Martínez-Aguilar J, et al. Multiplexed neural sensor array of graphene solution-gated field-effect transistors. 2D Mater, 2020, 7: 025046

    Article  Google Scholar 

  56. Shi Z F, Zheng F M, Zhou Z T, et al. Silk-enabled conformal multifunctional bioelectronics for investigation of spatiotemporal epileptiform activities and multimodal neural encoding/decoding. Adv Sci, 2019, 6: 1801617

    Article  Google Scholar 

  57. Ji B W, Guo Z J, Wang M H, et al. Flexible polyimide-based hybrid opto-electric neural interface with 16 channels of micro-LEDs and electrodes. Microsyst Nanoeng, 2018, 4: 1–11

    Article  Google Scholar 

  58. Tybrandt K, Khodagholy D, Dielacher B, et al. High-density stretchable electrode grids for chronic neural recording. Adv Mater, 2018, 30: 1706520

    Article  Google Scholar 

  59. Campbell P K, Jones K E, Huber R J, et al. A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array. IEEE Trans Biomed Eng, 1991, 38: 758–768

    Article  Google Scholar 

  60. Shobe J L, Claar L D, Parhami S, et al. Brain activity mapping at multiple scales with silicon microprobes containing 1024 electrodes. J NeuroPhysiol, 2015, 114: 2043–2052

    Article  Google Scholar 

  61. Jun J J, Steinmetz N A, Siegle J H, et al. Fully integrated silicon probes for high-density recording of neural activity. Nature, 2017, 551: 232–236

    Article  Google Scholar 

  62. Wei X L, Luan L, Zhao Z T, et al. Nanofabricated ultraflexible electrode arrays for high-density intracortical recording. Adv Sci, 2018, 5: 1700625

    Article  Google Scholar 

  63. Shin H, Son Y, Chae U, et al. Multifunctional multi-shank neural probe for investigating and modulating long-range neural circuits in vivo. Nat Commun, 2019, 10: 1–11

    Article  Google Scholar 

  64. Liu C B, Zhao Y, Cai X, et al. A wireless, implantable optoelectrochemical probe for optogenetic stimulation and dopamine detection. Microsyst Nanoeng, 2020, 6: 1–12

    Article  Google Scholar 

  65. Xiao G H, Song Y L, Zhang Y, et al. Microelectrode arrays modified with nanocomposites for monitoring dopamine and spike firings under deep brain stimulation in rat models of Parkinson’s disease. ACS Sens, 2019, 4: 1992–2000

    Article  Google Scholar 

  66. Wang M H, Gu X W, Ji B W, et al. Three-dimensional drivable optrode array for high-resolution neural stimulations and recordings in multiple brain regions. Biosens Bioelectron, 2019, 131: 9–16

    Article  Google Scholar 

  67. Rizk M, Bossetti C A, Jochum T A, et al. A fully implantable 96-channel neural data acquisition system. J Neural Eng, 2009, 6: 026002

    Article  Google Scholar 

  68. Liu X L, Zhang M L, Subei B, et al. The PennBMBI: design of a general purpose wireless brain-machine-brain interface system. IEEE Trans Biomed Circ Syst, 2015, 9: 248–258

    Article  Google Scholar 

  69. Bentler C, Stieglitz T. Building wireless implantable neural interfaces within weeks for neuroscientists. In: Proceedings of the 39th Engineering in Medicine and Biology Society (EMBC), 2017. 1078–1081

  70. Kanchwala M A, McCallum G A, Durand D M. A miniature wireless neural recording system for chronic implantation in freely moving animals. In: Proceedings of IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018

  71. Gagnon-Turcotte G, Gagnon L L, Bilodeau G, et al. Wireless brain computer interfaces enabling synchronized optogenetics and electrophysiology. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2017

  72. Shon A, Chu J U, Jung J, et al. An implantable wireless neural interface system for simultaneous recording and stimulation of peripheral nerve with a single cuff electrode. Sensors, 2018, 18: 1

    Article  Google Scholar 

  73. Muller R, Le H P, Li W, et al. A minimally invasive 64-channel wireless μECoG implant. IEEE J Solid-State Circ, 2015, 50: 344–359

    Article  Google Scholar 

  74. Liu X L, Zhu H J, Zhang M L, et al. A fully integrated wireless sensor-brain interface system to restore finger sensation. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2017

  75. Laiwalla F, Lee J, Lee A H, et al. A distributed wireless network of implantable sub-mm cortical microstimulators for brain-computer interfaces. In: Proceedings of the 41st Annual International Conference of IEEE Engineering in Medicine and Biology Society, 2019. 6876–6879

  76. Lopez C M, Putzeys J, Raducanu B 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 Circ Syst, 2017, 11: 510–522

    Article  Google Scholar 

  77. Zhang X, Pei W H, Huang B J, et al. A low-noise fully-differential CMOS preamplifier for neural recording applications. Sci China Inf Sci, 2012, 55: 441–452

    Article  MathSciNet  Google Scholar 

  78. Chang S I, Park S Y, Yoon E. Minimally-invasive neural interface for distributed wireless electrocorticogram recording systems. Sensors, 2018, 18: 263

    Article  Google Scholar 

  79. Liu S Y, Moncion C, Zhang J W, et al. Fully passive flexible wireless neural recorder for the acquisition of neuropotentials from a rat model. ACS Sens, 2019, 4: 3175–3185

    Article  Google Scholar 

  80. Yeon P, Bakir M S, Ghovanloo M. Towards a 1.1 mm 2 free-floating wireless implantable neural recording SoC. In: Proceedings of IEEE Custom Integrated Circuits Conference (CICC), 2018

  81. Kim C, Park J, Ha S, et al. A 3 mm×3 mm fully integrated wireless power receiver and neural interface system-on-chip. IEEE Trans Biomed Circ Syst, 2019, 13: 1736–1746

    Article  Google Scholar 

  82. Seo D, Neely R M, Shen K, et al. Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron, 2016, 91: 529–539

    Article  Google Scholar 

  83. Lee J, Laiwalla F, Jeong J, et al. Wireless power and data link for ensembles of sub-mm scale implantable sensors near 1 GHz. In: Proceedings of IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018

  84. Ghanbari M M, Piech D K, Shen K, et al. 17.5 A 0.8 mm 3 ultrasonic implantable wireless neural recording system with linear AM backscattering. In: Proceedings of IEEE International Solid-State Circuits Conference (ISSCC), 2019. 284–286

  85. Su Y, Routhu S, Moon K, et al. A wireless 32-channel implantable bidirectional brain machine interface. Sensors, 2016, 16: 1582

    Article  Google Scholar 

  86. Yoshimoto S, Araki T, Uemura T, et al. Implantable wireless 64-channel system with flexible ECoG electrode and optogenetics probe. In: Proceedings of IEEE Biomedical Circuits and Systems Conference (BioCAS), 2016. 476–479

  87. Lyu L, Ye D, Shi C J R. A 340 nW/channel 110 dB PSRR neural recording analog front-end using replica-biasing LNA, level-shifter assisted PGA, and averaged LFP servo loop in 65 nm CMOS. IEEE Trans Biomed Circ Syst, 2020, 14: 811–824

    Article  Google Scholar 

  88. Jang J W, Kim Y R, Lee C E, et al. A 32ch low power neural recording system with continuously monitoring for ECoG Signal detection. J Integr Circ Syst, 2021, 7: 2

    Google Scholar 

  89. Türe K, Dehollain C, Maloberti F. Wireless Power Transfer and Data Communication for Intracranial Neural Recording Applications. Berlin: Springer, 2020

    Book  Google Scholar 

  90. Thakor N V. Translating the brain-machine interface. Sci Transl Med, 2013, 5: 210ps17

    Article  Google Scholar 

  91. Shannon C E. Communication in the presence of noise. Proc IEEE, 1998, 86: 447–457

    Article  Google Scholar 

  92. Schuettler M, Kohler F, Ordonez J S, et al. Hermetic electronic packaging of an implantable brain-machine-interface with transcutaneous optical data communication. In: Proceedings of International Conference on Information Theoretic Security, 2012. 3886–3889

  93. Yin M, Borton D A, Komar J, et al. Wireless neurosensor for full-spectrum electrophysiology recordings during free behavior. Neuron, 2014, 84: 1170–1182

    Article  Google Scholar 

  94. Mestais C S, Charvet G, Sauter-Starace F, et al. WIMAGINE: wireless 64-channel ECoG recording implant for long term clinical applications. IEEE Trans Neural Syst Rehabil Eng, 2015, 23: 10–21

    Article  Google Scholar 

  95. Deshmukh A, Brown L, Barbe M F, et al. Fully implantable neural recording and stimulation interfaces: peripheral nerve interface applications. J Neurosci Method, 2020, 333: 108562

    Article  Google Scholar 

  96. Song Y K, Borton D A, Park S, et al. Active microelectronic neurosensor arrays for implantable brain communication interfaces. IEEE Trans Neural Syst Rehabil Eng, 2009, 17: 339–345

    Article  Google Scholar 

  97. Xu J, Nguyen A T, Zhao W F, et al. A low-noise, wireless, frequency-shaping neural recorder. IEEE J Emerg Sel Top Circ Syst, 2018, 8: 187–200

    Article  Google Scholar 

  98. Sharma D K, Mishra A, Saxena R. Analog & digital modulation techniques: an overview. Int J Comput Sci Commun Technol, 2010, 3: 2007

    Google Scholar 

  99. Idogawa S, Yamashita K, Sanda R, et al. A lightweight, wireless Bluetooth-low-energy neuronal recording system for mice. Sens Actuat B-Chem, 2021, 331: 129423

    Article  Google Scholar 

  100. Jia Y, Khan W, Lee B, et al. Wireless opto-electro neural interface for experiments with small freely behaving animals. J Neural Eng, 2018, 15: 046032

    Article  Google Scholar 

  101. Antonioli G, Baggioni F, Consiglio F, et al. Stinulatore cardiaco impiantabile con nuova battaria a stato solido al litio. Minerva Med, 1973, 64: 2298–2305

    Google Scholar 

  102. Zaeimbashi M, Nasrollahpour M, Khalifa A, et al. Ultra-compact dual-band smart NEMS magnetoelectric antennas for simultaneous wireless energy harvesting and magnetic field sensing. Nat Commun, 2021, 12: 1–11

    Article  Google Scholar 

  103. Lee B, Koripalli M K, Jia Y, et al. An implantable peripheral nerve recording and stimulation system for experiments on freely moving animal subjects. Sci Rep-Uk, 2018, 8: 1–12

    Google Scholar 

  104. Moon E, Barrow M, Lim J, et al. Bridging the “last millimeter” gap of brain-machine interfaces via near-infrared wireless power transfer and data communications. ACS Photonics, 2021, 8: 1430–1438

    Article  Google Scholar 

  105. Xie X, Rieth L, Williams L, et al. Long-term reliability of Al2O3 and Parylene C bilayer encapsulated Utah electrode array based neural interfaces for chronic implantation. J Neural Eng, 2014, 11: 026016

    Article  Google Scholar 

  106. Fang H, Zhao J N, Yu K J, et al. Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems. Proc Natl Acad Sci USA, 2016, 113: 11682–11687

    Article  Google Scholar 

  107. Shen K, Maharbiz M M. Ceramic packages for acoustically coupled neural implants. In: Proceedings of the 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019. 847–850

  108. Yao J L, Qiang W J, Wei H, et al. Ultrathin and robust micro-nano composite coating for implantable pressure sensor encapsulation. ACS Omega, 2020, 5: 23129–23139

    Article  Google Scholar 

  109. Bettinger C J, Ecker M, Kozai T D Y, et al. Recent advances in neural interfaces-materials chemistry to clinical translation. MRS Bull, 2020, 45: 655–668

    Article  Google Scholar 

  110. Sharma A, Rieth L, Tathireddy P, et al. Evaluation of the packaging and encapsulation reliability in fully integrated, fully wireless 100 channel Utah Slant electrode array (USEA): implications for long term functionality. Sens Actuat A-Phys, 2012, 188: 167–172

    Article  Google Scholar 

  111. Hwang G T, Im D, Lee S E, et al. In vivo silicon-based flexible radio frequency integrated circuits monolithically encapsulated with biocompatible liquid crystal polymers. ACS Nano, 2013, 7: 4545–4553

    Article  Google Scholar 

  112. Kiourti A, Lee C W L, Chae J, et al. A wireless fully passive neural recording device for unobtrusive neuropotential monitoring. IEEE Trans Biomed Eng, 2016, 63: 131–137

    Article  Google Scholar 

  113. Neely R M, Piech D K, Santacruz S R, et al. Recent advances in neural dust: towards a neural interface platform. Curr Opin NeuroBiol, 2018, 50: 64–71

    Article  Google Scholar 

  114. Luan H W, Zhang Y H. Programmable stimulation and actuation in flexible and stretchable electronics. Adv Intell Syst, 2021, 3: 2000228

    Article  Google Scholar 

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Acknowledgements

This work was supported by China Postdoctoral Science Foundation (Grant Nos. 2020TQ0246, 2021M692-638), Shanghai Sailing Program (Grant No. 21YF1451000), Fundamental Research Funds for the Central Universities (Grant No. 31020200QD013), and Natural Science Foundation of Chongqing (Grant No. cstc2021jcyj-msxmX0825).

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Authors and Affiliations

  1. Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, 710072, China

    Bowen Ji, Zekai Liang, Yuhao Zhou, Huicheng Feng & Honglong Chang

  2. Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Bowen Ji, Zekai Liang, Xichen Yuan, Yuhao Zhou, Huicheng Feng & Honglong Chang

  3. Collaborative Innovation Center of Northwestern Polytechnical University, Shanghai, 201108, China

    Bowen Ji & Yuhao Zhou

  4. NEURACLE TECH (China) Co., Ltd., Changzhou, 213100, China

    Honglai Xu

  5. College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China

    Minghao Wang

  6. Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, 100071, China

    Erwei Yin

  7. Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China

    Erwei Yin

  8. National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zhejun Guo, Longchun Wang & Jingquan Liu

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  1. Bowen Ji
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Correspondence to Honglong Chang or Jingquan Liu.

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Authors Ji B W and Liang Z K have the same contribution to this work.

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Ji, B., Liang, Z., Yuan, X. et al. Recent advances in wireless epicortical and intracortical neuronal recording systems. Sci. China Inf. Sci. 65, 140401 (2022). https://doi.org/10.1007/s11432-021-3373-1

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