CoChat
Sign Up
Cited by 118Neuroscience and Neural EngineeringAnalog and Mixed-Signal Circuit DesignEEG and Brain-Computer Interfaces

Power-saving design opportunities for wireless intracortical brain–computer interfaces

Open access
Even-Chen, Nir, Muratore, Dante G., Stavisky, Sergey D., Hochberg, Leigh R., Henderson, Jaimie M., Murmann, Boris, Shenoy, Krishna V.·2020·Nature Biomedical Engineering
DOI 10.1038/s41551-020-0595-9PMC PMC8286886
Open this paper in CoChat — free account

Abstract

The efficacy of wireless intracortical brain-computer interfaces (iBCIs) is limited in part by the number of recording channels, which is constrained by the power budget of the implantable system. Designing wireless iBCIs that provide the high-quality recordings of today's wired neural interfaces may lead to inadvertent over-design at the expense of power consumption and scalability. Here, we report analyses of neural signals collected from experimental iBCI measurements in rhesus macaques and from a clinical-trial participant with implanted 96-channel Utah multielectrode arrays to understand the trade-offs between signal quality and decoder performance. Moreover, we propose an efficient hardware design for clinically viable iBCIs, and suggest that the circuit design parameters of current recording iBCIs can be relaxed considerably without loss of performance. The proposed design may allow for an order-of-magnitude power savings and lead to clinically viable iBCIs with a higher channel count.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41551-020-0595-9

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The number of recording channels in iBCIs is limited by the power budget of the implantable system, and designing for lower bit error rate can improve power consumption.

    “For example, the number of recording channels in iBCIs is limited by the power budget of the implantable system, and designing for lower bit error rate (BER) can improve power consumption (Even-Chen et al., 2020).”

    From Comparative analysis of energy transfer mechanisms for neural implants (2024) · cites this paper as Even-Chen et al., 2020

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 94

  1. [1]Slutzky MW, “ Brain-Machine interfaces: Powerful tools for clinical treatment and neuroscientific investigations,” Neuroscientist, 5 2018. PMC6611552 29772957
  2. [2]Bouton CE, Shaikhouni A, Annetta NV, Bockbrader MA, Friedenberg DA, Nielson DM, Sharma G, Sederberg PB, Glenn BC, Mysiw WJ, Morgan AG, Deogaonkar M, and Rezai AR, “ Restoring cortical control of functional movement in a human with quadriplegia,” Nature, vol. 533, no. 7602, pp. 247– 250, 5 2016. 27074513
  3. [3]Ajiboye AB, Willett FR, Young DR, Memberg WD, Murphy BA, Miller JP, Walter BL, Sweet JA, Hoyen HA, Keith MW, Peckham PH, Simeral JD, Donoghue JP, Hochberg LR, and Kirsch RF, “ Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration,” Lancet, vol. 389, no. 10081, pp. 1821– 1830, 5 2017. 28363483 PMC5516547
  4. [5]Collinger JL, Boninger ML, Bruns TM, Curley K, Wang W, and Weber DJ, “ Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury,” J. Rehabil. Res. Dev, vol. 50, no. 2, pp. 145– 160, 2013. 23760996 PMC3684986
  5. [6]Downey JE, Weiss JM, Muelling K, Venkatraman A, Valois J-S, Hebert M, Bagnell JA, Schwartz AB, and Collinger JL, “ Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping,” J. Neuroeng. Rehabil, vol. 13, no. 28, 3. 2016. PMC4797113 26987662
  6. [7]Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, and Donoghue JP, “ Neuronal ensemble control of prosthetic devices by a human with tetraplegia,” Nature, vol. 442, no. 7099, pp. 164– 171, 7. 2006. 16838014
  7. [10]Blabe CH, Gilja V, Chestek CA, Shenoy KV, Anderson KD, and Henderson JM, “ Assessment of brain–machine interfaces from the perspective of people with paralysis,” J. Neural Eng, vol. 12, no. 4, p. 043002, 2015. 26169880 PMC4761228
  8. [11]Gilja V, Pandarinath C, Blabe CH, Nuyujukian P, Simeral JD, Sarma AA, Sorice BL, Perge JA, Jarosiewicz B, Hochberg LR, Shenoy KV, and Henderson JM, “ Clinical translation of a high-performance neural prosthesis,” Nat. Med, vol. 21, no. 10, pp. 1142– 1145, 10. 2015. 26413781 PMC4805425
  9. [13]Lebedev MA and Nicolelis MAL, “ Brain-Machine interfaces: From basic science to neuroprostheses and neurorehabilitation,” Physiol. Rev, vol. 97, no. 2, pp. 767– 837, 4. 2017. 28275048
  10. [14]Schwarz DA, Lebedev MA, Hanson TL, Dimitrov DF, Lehew G, Meloy J, Rajangam S, Subramanian V, Ifft PJ, Li Z, Ramakrishnan A, Tate A, Zhuang KZ, and Nicolelis MAL, “ Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys,” Nat. Methods, vol. 11, no. 6, pp. 670– 676, 6. 2014. 24776634 PMC4161037
  11. [16]Gao P and Ganguli S, “ On simplicity and complexity in the brave new world of large-scale neuroscience,” Curr. Opin. Neurobiol, vol. 32, pp. 148– 155, 6. 2015. 25932978
  12. [17]Wodlinger B, Downey JE, Tyler-Kabara EC, Schwartz AB, Boninger ML, and Collinger JL, “ Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations,” J. Neural Eng, vol. 12, no. 1, p. 016011, 2. 2015. 25514320
  13. [18]Mitz AR, Bartolo R, Saunders RC, Browning PG, Talbot T, and Averbeck BB, “ High channel count single-unit recordings from nonhuman primate frontal cortex,” J. Neurosci. Methods, vol. 289, pp. 39– 47, 7. 2017. 28687520 PMC5584609
  14. [20]Gao H, Walker RM, Nuyujukian P, Makinwa KAA, Shenoy KV, Murmann B, and Meng TH, “ HermesE: A 96-channel full data rate direct neural interface in 0.13 μm CMOS,” IEEE J. Solid-State Circuits, vol. 47, no. 4, pp. 1043– 1055, 4. 2012.
  15. [21]Miranda H, Gilja V, Chestek CA, Shenoy KV, and Meng TH, “ HermesD: A high-rate long-range wireless transmission system for simultaneous multichannel neural recording applications,” IEEE Trans. Biomed. Circuits Syst, vol. 4, no. 3, pp. 181– 191, 6. 2010. 23853342
  16. [22]Borton DA, Yin M, Aceros J, and Nurmikko A, “ An implantable wireless neural interface for recording cortical circuit dynamics in moving primates,” J. Neural Eng, vol. 10, no. 2, p. 026010, 4. 2013. 23428937 PMC3638022
  17. [23]Yin M, Borton DA, Komar J, Agha N, Lu Y, Li H, Laurens J, Lang Y, Li Q, Bull C, Larson L, Rosler D, Bezard E, Courtine G, and Nurmikko AV, “ Wireless neurosensor for full-spectrum electrophysiology recordings during free behavior,” Neuron, vol. 84, no. 6, pp. 1170– 1182, 12. 2014. 25482026
  18. [24]Kao JC, Stavisky SD, Sussillo D, Nuyujukian P, and Shenoy KV, “ Information systems opportunities in Brain-Machine interface decoders,” Proc. IEEE, vol. 102, no. 5, pp. 666– 682, 5 2014.
  19. [25]Fraser GW, Chase SM, Whitford A, and Schwartz AB, “ Control of a brain-computer interface without spike sorting,” J. Neural Eng, vol. 6, no. 5, p. 055004, 10. 2009. 19721186
  20. [26]Perel S, Sadtler PT, Oby ER, Ryu SI, Tyler-Kabara EC, Batista AP, and Chase SM, “ Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics,” J. Neurophysiol, vol. 114, no. 3, pp. 1500– 1512, 9. 2015. 26133797 PMC4556850
  21. [27]Christie BP, Tat DM, Irwin ZT, Gilja V, Nuyujukian P, Foster JD, Ryu SI, Shenoy KV, Thompson DE, and Chestek CA, “ Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance,” J. Neural Eng, vol. 12, no. 1, p. 016009, 2. 2015. 25504690 PMC4332592
  22. [28]Li J and Li Z, “ Sums of spike waveform features for motor decoding,” Front. Neurosci, vol. 11, p. 406, 2017. 28769745 PMC5513987
  23. [29]Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT, O’Shea DJ, Vyas S, Sun X, Ryu SI, Ganguli S, and Shenoy KV, “ Accurate estimation of neural population dynamics without spike sorting,” Neuron, vol. 103, no. 2, pp. 292– 308.e4, 6. 2019. [Online]. Available: 10.1016/j.neuron.2019.05.003 31171448 PMC7002296 · DOI · Paper page
  24. [30]Han D, Zheng Y, Rajkumar R, Dawe G, and Je M, “ A 0.45v 100-channel neural-recording IC with sub-μw/channel consumption in 0.18μm CMOS,” in IEEE International Solid-State Circuits Conference (ISSCC), 2. 2013, pp. 291– 292.
  25. [31]Irwin ZT, Thompson DE, Schroeder KE, Tat DM, Hassani A, Bullard AJ, Woo SL, Urbanchek MG, Sachs AJ, Cederna PS, Stacey WC, Patil PG, and Chestek CA, “ Enabling Low-Power, Multi-Modal neural interfaces through a common, Low-Bandwidth feature space,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 24, no. 5, pp. 521– 531, 5 2016. 26600160
  26. [32]Sodagar AM, Wise KD, and Najafi K, “ A fully integrated mixed-signal neural processor for implantable multichannel cortical recording,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 6, pp. 1075– 1088, 2007. 17554826
  27. [33]Karkare V, Gibson S, and Marković D, “ A 75-μw, 16-channel neural Spike-Sorting processor with unsupervised clustering,” IEEE J. Solid-State Circuits, vol. 48, no. 9, pp. 2230– 2238, 9. 2013.
  28. [34]Muratore DG, Tandon P, Wootters M, Chichilnisky EJ, Mitra S, and Murmann B, “ A Data-Compressive Wired-OR readout for massively parallel neural recording,” IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 6, pp. 1128– 1140, 12. 2019. 31425051
  29. [35]Aprile C, Ture K, Baldassarre L, Shoaran M, Yilmaz G, Maloberti F, Dehollain C, Leblebici Y, and Cevher V, “ Adaptive Learning-Based compressive sampling for low-power wireless implants,” IEEE Trans. Circuits Syst. I Regul. Pap, vol. 65, no. 11, pp. 3929– 3941, 2018.
  30. [36]Pagin M and Ortmanns M, “ A neural data lossless compression scheme based on spatial and temporal prediction,” in IEEE Biomedical Circuits and Systems Conference (BioCAS), 2017, pp. 1– 4.30406220
  31. [37]Wu T, Zhao W, Keefer E, and Yang Z, “ Deep compressive autoencoder for action potential compression in large-scale neural recording,” J. Neural Eng, vol. 15, no. 6, p. 066019, 2018. 30215605
  32. [38]Okazawa T and Akita I, “ A Time-Domain analog spatial compressed sensing encoder for Multi-Channel neural recording,” Sensors, vol. 18, no. 1, 2018. PMC5795473 29324675
  33. [39]Shoaran M, Lopez MM, Pasupureddi VSR, Leblebici Y, and Schmid A, “ A low-power area-efficient compressive sensing approach for multi-channel neural recording,” in IEEE International Symposium on Circuits and Systems (ISCAS), 5 2013, pp. 2191– 2194.
  34. [40]Musk E and Neuralink, “ An integrated brain-machine interface platform with thousands of channels,” bioRxiv, p. 703801, 7. 2019. PMC6914248 31642810
  35. [42]Lopez CM, Putzeys J, Raducanu BC, Ballini M, Wang S, Andrei A, Rochus V, Vandebriel R, Severi S, Van Hoof C, Musa S, Van Helleputte N, Yazicioglu RF, and Mitra S, “ A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13μm SOI CMOS,” IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 3, pp. 510– 522, 2017. 28422663
  36. [43]De Dorigo D, Moranz C, Graf H, Marx M, Wendler D, Shui B, Sayed Herbawi A, Kuhl M, Ruther P, Paul O, and Manoli Y, “ Fully immersible subcortical neural probes with modular architecture and a Delta-Sigma ADC integrated under each electrode for parallel readout of 144 recording sites,” IEEE J. Solid-State Circuits, vol. 53, no. 11, pp. 3111– 3125, 11. 2018.
  37. [44]Lee B, Jia Y, Mirbozorgi SA, Connolly M, Tong X, Zeng Z, Mahmoudi B, and Ghovanloo M, “ An Inductively-Powered wireless neural recording and stimulation system for Freely-Behaving animals,” IEEE Trans. Biomed. Circuits Syst, vol. 13, no. 2, pp. 413– 424, 2019. 30624226 PMC6510586
  38. [45]Angotzi GN, Boi F, Lecomte A, Miele E, Malerba M, Zucca S, and Casile, “ SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings,” Biosensors and Bioelectronics, vol. 126, pp. 355– 364, 2. 2019. 30466053
  39. [46]Fiáth R, Raducanu BC, Musa S, Andrei A, Lopez CM, van Hoof C, Ruther P, Aarts A, Horváth D, and Ulbert I, “ A silicon-based neural probe with densely-packed low-impedance titanium nitride microelectrodes for ultrahigh-resolution in vivo recordings,” Biosens. Bioelectron, vol. 106, pp. 86– 92, 5 2018. 29414094
  40. [47]Einevoll GT, Kayser C, Logothetis NK, and Panzeri S, “ Modelling and analysis of local field potentials for studying the function of cortical circuits,” Nat. Rev. Neurosci, vol. 14, no. 11, pp. 770– 785, 11. 2013. 24135696
  41. [48]Belitski A, Gretton A, Magri C, Murayama Y, Montemurro MA, Logothetis NK, and Panzeri S, “ Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information,” J. Neurosci, vol. 28, no. 22, pp. 5696– 5709, 5 2008. 18509031 PMC6670798
  42. [50]Chestek CA, Gilja V, Nuyujukian P, Foster JD, Fan JM, Kaufman MT, Churchland MM, Rivera-Alvidrez Z, Cunningham JP, Ryu SI, and Shenoy KV, “ Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex,” J. Neural Eng, vol. 8, no. 4, p. 045005, 8. 2011. 21775782 PMC3644617
  43. [51]Todorova S, Sadtler P, Batista A, Chase S, and Ventura V, “ To sort or not to sort: the impact of spike-sorting on neural decoding performance,” J. Neural Eng, vol. 11, no. 5, p. 056005, 10. 2014. 25082508 PMC4454741
  44. [52]Kao JC, Nuyujukian P, Ryu SI, and Shenoy KV, “ A High-Performance neural prosthesis incorporating discrete state selection with hidden markov models,” IEEE Trans. Biomed. Eng, vol. 64, no. 4, pp. 935– 945, 4. 2017. 27337709
  45. [53]Shanechi MM, Orsborn AL, Moorman HG, Gowda S, Dangi S, and Carmena JM, “ Rapid control and feedback rates enhance neuroprosthetic control,” Nat. Commun, vol. 8, p. 13825, 1. 2017. 28059065 PMC5227098
  46. [54]Even-Chen N, Stavisky SD, Kao JC, Ryu SI, and Shenoy KV, “ Augmenting intracortical brain-machine interface with neurally driven error detectors,” J. Neural Eng, vol. 14, no. 6, p. 066007, 11. 2017. 29130452 PMC5742283
  47. [56]Muelling K, Venkatraman A, Valois J-S, Downey J, Weiss J, Javdani S, Hebert M, Schwartz AB, Collinger JL, and Andrew Bagnell J, “ Autonomy infused teleoperation with application to BCI manipulation,” arXiv, vol. cs.RO, p. 1503.05451, 3. 2015.
  48. [57]Gilja V, Nuyujukian P, Chestek CA, Cunningham JP, Yu BM, Fan JM, Churchland MM, Kaufman MT, Kao JC, Ryu SI, and Shenoy KV, “ A high-performance neural prosthesis enabled by control algorithm design,” Nat. Neurosci, vol. 15, no. 12, pp. 1752– 1757, 12. 2012. 23160043 PMC3638087
  49. [58]Katyal KD, Johannes MS, Kellis S, Aflalo T, Klaes C, McGee TG, Para MP, Shi Y, Lee B, Pejsa K, Liu C, Wester BA, Tenore F, Beaty JD, Ravitz AD, Andersen RA, and McLoughlin MP, “ A collaborative BCI approach to autonomous control of a prosthetic limb system,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 10. 2014, pp. 1479– 1482.
  50. [59]Oby ER, Perel S, Sadtler PT, Ruff DA, Mischel JL, Montez DF, Cohen MR, Batista AP, and Chase SM, “ Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters,” J. Neural Eng, vol. 13, no. 3, p. 036009, 6. 2016. 27097901 PMC5931220
  51. [60]Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, and Shenoy KV, “ A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes,” J. Neural Eng, vol. 12, no. 3, p. 036009, 5 2015. 25946198 PMC4457459
  52. [61]Flint RD, Lindberg EW, Jordan LR, Miller LE, and Slutzky MW, “ Accurate decoding of reaching movements from field potentials in the absence of spikes,” J. Neural Eng, vol. 9, no. 4, p. 046006, 8. 2012. 22733013 PMC3429374
  53. [62]Even-Chen N, Stavisky SD, Pandarinath C, Nuyujukian P, Blabe CH, Hochberg LR, Henderson JM, and Shenoy KV, “ Feasibility of automatic error Detect-and-Undo system in human intracortical Brain-Computer interfaces,” IEEE Trans. Biomed. Eng, vol. 65, no. 8, pp. 1771– 1784, 8. 2018. 29989931
  54. [63]Brandman DM, Burkhart MC, Kelemen J, Franco B, Harrison MT, and Hochberg LR, “ Robust Closed-Loop control of a cursor in a person with tetraplegia using gaussian process regression,” Neural Comput, pp. 1– 23, 9. 2018. PMC6685768 30216140
  55. [64]Fernández E and Botella P, “ Biotolerability of intracortical microelectrodes,” Adv. Biosys, vol. 2, no. 1, p. 1700115, 1. 2018.
  56. [65]Zhai S, Hunter M, and Smith BA, “ Performance optimization of virtual keyboards,” Human–Computer Interaction, vol. 17, no. 2-3, pp. 229– 269, 9. 2002.
  57. [66]Zumsteg ZS, Kemere C, O’Driscoll S, Santhanam G, Ahmed RE, Shenoy KV, and Meng TH, “ Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 13, no. 3, pp. 272– 279, 9. 2005. 16200751
  58. [67]Kao JC, Nuyujukian P, Ryu SI, Churchland MM, Cunningham JP, and Shenoy KV, “ Single-trial dynamics of motor cortex and their applications to brain-machine interfaces,” Nat. Commun, vol. 6, p. 7759, 7. 2015. 26220660 PMC4532790
  59. [68]Shanechi MM, “ Brain-Machine interface control algorithms,” IEEE Trans. Neural Syst. Rehabil. Eng, 12. 2016. 28113323
  60. [69]Sussillo D, Stavisky SD, Kao JC, Ryu SI, and Shenoy KV, “ Making brain-machine interfaces robust to future neural variability,” Nat. Commun, vol. 7, p. 13749, 12. 2016. 27958268 PMC5159828
  61. [70]Glaser JI, Chowdhury RH, Perich MG, Miller LE, and Kording KP, “ Machine learning for neural decoding,” arXiv:1708.00909v2, 8. 2017. PMC7470933 32737181 · arXiv
  62. [71]Cunningham JP, Gilja V, Ryu SI, and Shenoy KV, “ Methods for estimating neural firing rates, and their application to brain–machine interfaces,” Neural Netw, vol. 22, no. 9, pp. 1235– 1246, 11. 2009. 19349143 PMC2783748
  63. [72]Perge JA, Homer ML, Malik WQ, Cash S, Eskandar E, Friehs G, Donoghue JP, and Hochberg LR, “ Intra-day signal instabilities affect decoding performance in an intracortical neural interface system,” J. Neural Eng, vol. 10, no. 3, p. 036004, 6. 2013. 23574741 PMC3693851
  64. [74]Bahrami H, Mirbozorgi SA, Rusch LA, and Gosselin B, “ BER performance of implant-to-air high-speed UWB data communications for neural recording systems,” IEEE Proceedings of Engineering in Medicine and Biology Society Conference, pp. 3961– 3964, 8. 2014. 25570859
  65. [75]Ebrazeh A and Mohseni P, “ 30 pJ/b, 67 Mbps, centimeter-to-meter range data telemetry with an IR-UWB wireless link,” IEEE Transactions on Biomedical Circuits and Systems, vol. 9, no. 3, pp. 362– 369, 2015. 25134088
  66. [76]Harrison RR, Kier RJ, Chestek CA, Gilja V, Nuyujukian P, Ryu S, Greger B, Solzbacher F, and Shenoy KV, “ Wireless neural recording with single low-power integrated circuit,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 17, no. 4, pp. 322– 329, 8. 2009. 19497825 PMC2941647
  67. [77]Walden RH, “ Analog-to-digital converter survey and analysis,” IEEE Journal on selected areas in communications, vol. 17, no. 4, pp. 539– 550, 1999.
  68. [78]Gibson S, Chandler R, Karkare V, Markovic D, and Judy JW, “ An efficiency comparison of analog and digital spike detection,” in 2009 4th International IEEE/EMBS Conference on Neural Engineering, 4. 2009, pp. 423– 428.
  69. [79]Gibson S, Judy JW, and MarkoviÇ D, “ Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 18, no. 5, pp. 469– 478, 10. 2010. 20525534
  70. [80]Yang Z, Zhao Q, Keefer E, and Liu W, “ Noise characterization, modeling, and reduction for in vivo neural recording,” Advances in Neural Information Processing Systems, pp. 2160– 2168, 2009.
  71. [81]Chandrakumar H and MarkoviÇ D, “ An 80-mVpp linear-input range, 1.6-GΩ input impedance, low-power chopper amplifier for closed-loop neural recording that is tolerant to 650-mVpp common-mode interference,” IEEE Journal of Solid-State Circuits, vol. 52, no. 11, pp. 2811– 2828, 2017.
  72. [82]Mendrela AE, Cho J, Fredenburg JA, Nagaraj V, Netoff TI, Flynn MP, and Yoon E, “ A bidirectional neural interface circuit with active stimulation artifact cancellation and cross-channel common-mode noise suppression,” IEEE Journal of Solid-State Circuits, vol. 51, no. 4, pp. 955– 965, 2016.
  73. [83]Muller R, Gambini S, and Rabaey JM, “ A 0.013 mm2, 5 μW, dc-coupled neural signal acquisition ic with 0.5 v supply,” IEEE Journal of Solid-State Circuits, vol. 47, no. 1, pp. 232– 243, 2012.
  74. [84]Steyaert MS and Sansen WM, “ A micropower low-noise monolithic instrumentation amplifier for medical purposes,” IEEE Journal of Solid-State Circuits, vol. 22, no. 6, pp. 1163– 1168, 1987.
  75. [85]Kim S-J, Liu L, Yao L, Goh WL, Gao Y, and Je M, “ A 0.5-v sub-μ/channel neural-recording ic with delta-modulation-based spike detection,” in IEEE Asian Solid-State Circuits Conference (A-SSCC), 2014, pp. 189– 192.
  76. [86]Dong H, Yuanjin Z, Rajkumar R, Dawe G, and Minkyu J, “ 0.45 v 100-channel neural-recording ic with sub-mw/channel consumption in 0.18 mm cmos,” in IEEE International Solid-State Circuits Conference (ISSCC), 2013, pp. 17– 21.
  77. [87]Muller R, “ Low power, scalable platforms for implantable neural recording,” Ph.D. dissertation, University of California at Berkeley, 5 2015.
  78. [88]McCreary JL and Gray PR, “ All-MOS charge redistribution analog-to-digital conversion techniques.” IEEE Journal of Solid-State Circuits, vol. 10, no. 6, pp. 371– 379, 1975.
  79. [89]Karkare V, Chandrakumar H, RozgiÇ D, and MarkoviÇ D, “ Robust, reconfigurable, and power-efficient biosignal recording systems,” in IEEE Custom Integrated Circuits Conference (CICC), 2014, pp. 1– 8.
  80. [90]Goldsmith A, Wireless communications. Cambridge university press, 2005.
  81. [91]Miranda H and Meng TH, “ A programmable pulse uwb transmitter with 34energy efficiency for multichannel neuro-recording systems,” in IEEE Custom Integrated Circuits Conference, 9. 2010, pp. 1– 4.
  82. [92]Obeid I and Wolf PD, “ Evaluation of spike-detection algorithms for a Brain-Machine interface application,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 905– 911, 6. 2004. 15188857
  83. [93]Kaiser JF, “ On a simple algorithm to calculate the energy of a signal,” in Proceedings IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP), vol. 1, 1990, pp. 381– 384.
  84. [94]Chase SM, Schwartz AB, and Kass RE, “ Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain–computer interface algorithms,” Neural Netw, vol. 22, no. 9, pp. 1203– 1213, 11. 2009. 19502004 PMC2783655
  85. [97]Liberti WA, Perkins LN, Leman DP, and Gardner TJ, “ An open source, wireless capable miniature microscope system,” J. Neural Eng, vol. 14, no. 4, p. 045001, 8. 2017. 28514229 PMC5955387
  86. [98]Foster JD, Nuyujukian P, Freifeld O, Gao H, Walker R, I Ryu S, H Meng T, Murmann B, J Black M, and Shenoy KV, “ A freely-moving monkey treadmill model,” J. Neural Eng, vol. 11, no. 4, p. 046020, 8. 2014. 24995476
  87. [99]Cunningham JP, Nuyujukian P, Gilja V, Chestek CA, Ryu SI, and Shenoy KV, “ A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces,” J. Neurophysiol, vol. 105, no. 4, pp. 1932– 1949, 4. 2011. 20943945 PMC3075301
  88. [100]Santhanam G, Ryu SI, Yu BM, Afshar A, and Shenoy KV, “ A high-performance brain-computer interface,” Nature, vol. 442, no. 7099, pp. 195– 198, 7. 2006. 16838020
  89. [101]Jindal R, “ Compact noise models for mosfets,” IEEE Transactions on Electron Devices, vol. 53, no. 9, pp. 2051– 2061, 8. 2006.
  90. [102]Scholten A, Tiemeijer L, van Langevelde R, Havens R, van Duijnhoven AZ, and Venezia V, “ Noise modeling for RF CMOS circuit simulation,” IEEE Transactions on Electron Devices, vol. 50, no. 3, pp. 618– 632, 6. 2003.
  91. [103]Hariprasath V, Guerber J, Lee S-H, and Moon U-K, “ Merged capacitor switching based SAR ADC with highest switching energy-efficiency,” Electronics letters, vol. 46, no. 9, pp. 620– 621, 2010.
  92. [104]Razavi B, “ The strongarm latch [a circuit for all seasons],” IEEE Solid-State Circuits Magazine, vol. 7, no. 2, pp. 12– 17, 2015.
  93. [106]Chandrakumar H and Markovic D, “ A 15.2-ENOB continuous-time ΣΔ ADC for a 200mV pp-linear-input-range neural recording front-end,” in IEEE International Solid-State Circuits Conference-(ISSCC), 2. 2018, pp. 232– 234.
  94. [107]Murmann B, “ The race for the extra decibel: A brief review of current adc performance trajectories,” IEEE Solid State Circuits Magazine, vol. 7, no. 3, pp. 58– 66, Summer 2015.

Cited by

Sorted by citations

Authors
Even-Chen, N., Muratore, D. G., Stavisky, S. D., Hochberg, L. R., Henderson, J. M., Murmann, B., & Shenoy, K. V.
Affiliations
  • Stanford University (US)
  • Stanford Medicine (US)
  • Harvard University (US)
  • Howard Hughes Medical Institute (US)
Corresponding author
Even-Chen, Nir
ORCID
Venue
Nature Biomedical Engineering
Publisher
Springer Science and Business Media LLC
Published
2020-08-03
Volume
4
Issue
10
Pages
984-996
ISSN
2157-846X (ISSN-L)
DOI
10.1038/s41551-020-0595-9
PMC
PMC8286886
OpenAlex
W3047193243
Cited by
118
References
13
Citations by year
2026: 15 · 2025: 16 · 2024: 28 · 2023: 24 · 2022: 14 · 2021: 19 · 2020: 2
FWCI
6.71
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Howard Hughes Medical Institute
  • U.S. Department of Veterans Affairs (I01 RX002295, I50RX002864)
  • National Institutes of Health (5uh2ns095548-02)
  • National Institute on Deafness and Other Communication Disorders (R01DC009899, R01DC014034)
  • National Institute of Neurological Disorders and Stroke (UH2-NS095548, U01NS098968)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP1HD075623)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
intracortical brain-computer interfaces, wireless neural recording, power consumption, implantable neural interfaces, signal quality, rhesus monkeys
MeSH terms
Animals, Electrodes, Implanted, Equipment Design, Hand, Humans, Macaca mulatta, Male, Middle Aged, Electric Power Supplies, Wireless Technology, Brain-Computer Interfaces

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review