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A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces

Abstract

The large power requirement of current brain–machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300–1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.

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Fig. 1: Representation of spikes in the 300–1,000 Hz band.
Fig. 2: SBP, TCR (optimized threshold set at −3.75 × r.m.s.) and low-bandwidth TCR (optimized threshold set at 2.75 × r.m.s.) prediction of true firing rate.
Fig. 3: Correlation between the true firing rate of individual units and the field’s SBP.
Fig. 4: Comparison between the decoding performances of SBP and TCR.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.

Code availability

The code used in this study is available from the corresponding author upon reasonable request.

References

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

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008).

    CAS  PubMed  Google Scholar 

  4. Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Rizk, M. et al. A fully implantable 96-channel neural data acquisition system. J. Neural Eng. 6, 026002 (2009).

    PubMed  PubMed Central  Google Scholar 

  6. 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).

    PubMed  PubMed Central  Google Scholar 

  7. Harrison, R. R. et al. A low-power integrated circuit for a wireless 100-electrode neural recording system. IEEE J. Solid State Circuits 42, 123–133 (2007).

    Google Scholar 

  8. Chae, M. et al. A 128-channel 6 mW wireless neural recording IC with on-the-fly spike sorting and UWB Tansmitter. IEEE Int. Solid State Circuits Conf. 51, 146–148 (2008).

    Google Scholar 

  9. Aziz, J. N. et al. 256-channel neural recording and delta compression microsystem with 3D electrodes. IEEE J. Solid State Circuits 44, 995–1005 (2009).

    Google Scholar 

  10. Shahrokhi, F., Abdelhalim, K., Serletis, D., Carlen, P. L. & Genov, R. The 128-channel fully differential digital integrated neural recording and stimulation interface. IEEE Trans. Biomed. Circuits Syst. 4, 149–161 (2010).

    PubMed  Google Scholar 

  11. Wattanapanitch, W. & Sarpeshkar, R. A low-power 32-channel digitally programmable neural recording integrated circuit. IEEE Trans. Biomed. Circuits Syst. 5, 592–602 (2011).

    CAS  PubMed  Google Scholar 

  12. Gao, H. et al. HermesE: a 96-channel full data rate direct neural interface in 0.13 μm CMOS. IEEE J. Solid State Circuits 47, 1043–1055 (2012).

    Google Scholar 

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

    Google Scholar 

  14. Abdelhalim, K., Kokarovtseva, L., Velazquez, J. L. P. & Genov, R. 915-MHz FSK/OOK wireless neural recording SoC with 64 mixed-signal FIR filters. IEEE J. Solid State Circuits 48, 2478–2493 (2013).

    Google Scholar 

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

    Google Scholar 

  16. Borna, A. & Najafi, K. A low power light weight wireless multichannel microsystem for reliable neural recording. IEEE J. Solid State Circuits 49, 439–451 (2014).

    Google Scholar 

  17. Limnuson, K., Lu, H., Chiel, H. J. & Mohseni, P. A bidirectional neural interface SoC with an integrated spike recorder, microstimulator, and low-power processor for real-time stimulus artifact rejection. Analog Integr. Circuits Signal Process. 82, 457–470 (2015).

    Google Scholar 

  18. Park, S. Y., Cho, J., Na, K. & Yoon, E. Modular 128-channel ΔΔΣ analog front-end architecture using spectrum equalization scheme for 1024-channel 3-D neural recording microsystems. IEEE J. Solid State Circuits 53, 501–514 (2018).

    Google Scholar 

  19. Harrison, R. R. et al. Wireless neural recording with single low-power integrated circuit. IEEE Trans. Neural Syst. Rehabilitation Eng. 17, 322–329 (2009).

    Google Scholar 

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

    Google Scholar 

  21. 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).

  22. Ethier, C., Oby, E. R., Bauman, M. J. & Miller, L. E. Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 485, 368–371 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Wodlinger, B. et al. Ten-dimensional anthropomorphic arm control in a human brain–machine interface: difficulties, solutions, and limitations. J. Neural Eng. 12, 016011 (2015).

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  25. 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).

    PubMed  PubMed Central  Google Scholar 

  26. Irwin, Z. T. et al. Neural control of finger movement via intracortical brain–machine interface. J. Neural Eng. 14, 066004 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017).

    PubMed  PubMed Central  Google Scholar 

  28. Shanechi, M. M. et al. Rapid control and feedback rates enhance neuroprosthetic control. Nat. Commun. 8, 13825 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Heldman, D. A., Wang, W., Chan, S. S. & Moran, D. W. Local field potential spectral tuning in motor cortex during reaching. IEEE Trans. Neural Syst. Rehabilitation Eng. 14, 180–183 (2006).

    Google Scholar 

  30. Bansal, A. K., Vargas-Irwin, C. E., Truccolo, W. & Donoghue, J. P. Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices. J. Neurophysiol. 105, 1603–1619 (2011).

    PubMed  PubMed Central  Google Scholar 

  31. Mollazadeh, M. et al. Spatiotemporal variation of multiple neurophysiological signals in the primary motor cortex during dexterous reach-to-grasp movements. J. Neurosci. 31, 15531–15543 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Stark, E. & Abeles, M. Predicting movement from multiunit activity. J. Neurosci. 27, 8387–8394 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Baker, J. et al. Multi-scale recordings for neuroprosthetic control of finger movements. In 2009 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 4573–4577 (IEEE, 2009).

  34. Zhuang, J., Truccolo, W., Vargas-Irwin, C. & Donoghue, J. P. Decoding 3-D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex. IEEE Trans. Biomed. Eng. 57, 1774–1784 (2010).

    PubMed  PubMed Central  Google Scholar 

  35. Flint, R. D., Ethier, C., Oby, E. R., Miller, L. E. & Slutzky, M. W. Local field potentials allow accurate decoding of muscle activity. J. Neurophysiol. 108, 18–24 (2012).

    PubMed  PubMed Central  Google Scholar 

  36. Flint, R. D., Lindberg, E. W., Jordan, L. R., Miller, L. E. & Slutzky, M. W. Accurate decoding of reaching movements from field potentials in the absence of spikes. J. Neural Eng. 9, 046006 (2012).

    PubMed  PubMed Central  Google Scholar 

  37. Aggarwal, V., Mollazadeh, M., Davidson, A. G., Schieber, M. H. & Thakor, N. V. State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements. J. Neurophysiol. 109, 3067–3081 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. Perge, J. A. et al. Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex. J. Neural Eng. 11, 046007 (2014).

    PubMed  PubMed Central  Google Scholar 

  39. Wang, D. et al. Long-term decoding stability of local field potentials from silicon arrays in primate motor cortex during a 2D center out task. J. Neural Eng. 11, 036009 (2014).

    PubMed  Google Scholar 

  40. Flint, R. D., Wright, Z. A., Scheid, M. R. & Slutzky, M. W. Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J. Neural Eng. 10, 056005 (2013).

    PubMed  PubMed Central  Google Scholar 

  41. So, K., Dangi, S., Orsborn, A. L., Gastpar, M. C. & Carmena, J. M. Subject-specific modulation of local field potential spectral power during brain–machine interface control in primates. J. Neural Eng. 11, 026002 (2014).

    PubMed  Google Scholar 

  42. Stavisky, S. D., Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes. J. Neural Eng. 12, 036009 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. Milekovic, T. et al. Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. J. Neurophysiol. 120, 343–360 (2018).

    PubMed  PubMed Central  Google Scholar 

  44. Kubánek, J., Miller, K. J., Ojemann, J. G., Wolpaw, J. R. & Schalk, G. Decoding flexion of individual fingers using electrocorticographic signals in humans. J. Neural Eng. 6, 066001 (2009).

    PubMed  PubMed Central  Google Scholar 

  45. Chao, Z. C., Nagasaka, Y. & Fujii, N. Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front. Neuroeng. 3, 1–10 (2010).

    Google Scholar 

  46. Chestek, C. A. et al. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas. J. Neural Eng. 10, 026002 (2013).

    PubMed  PubMed Central  Google Scholar 

  47. Flint, R., Rosenow, J., Tate, M. & Slutzky, M. Continuous decoding of human grasp kinematics using epidural and subdural signals. J. Neural Eng. 14, 016005 (2017).

    PubMed  Google Scholar 

  48. Hotson, G. et al. Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject. J. Neural Eng. 13, 026017 (2016).

    PubMed  PubMed Central  Google Scholar 

  49. Schlag, J. & Balvin, R. Background activity in the cerebral cortex and reticular formation in relation with the electroencephalogram. Exp. Neurol. 8, 203–219 (1963).

    Google Scholar 

  50. Irwin, Z. T. et al. Enabling low-power, multi-modal neural interfaces through a common, low-bandwidth feature space. IEEE Trans. Neural Syst. Rehabilitation Eng. 24, 521–531 (2016).

    Google Scholar 

  51. Kaufman, M. T. et al. The roles of monkey premotor neuron classes in movement preparation and execution. J. Neurophysiol. 104, 799–809 (2010).

    PubMed  PubMed Central  Google Scholar 

  52. Kaufman, M. T., Churchland, M. M. & Shenoy, K. V. The roles of monkey M1 neuron classes in movement preparation and execution. J. Neurophysiol. 110, 817–825 (2013).

    PubMed  PubMed Central  Google Scholar 

  53. Lempka, S. F. et al. Theoretical analysis of intracortical microelectrode recordings. J. Neural Eng. 8, 045006 (2011).

    PubMed  PubMed Central  Google Scholar 

  54. Holt, G. R. & Koch, C. Electrical interactions via the extracellular potential near cell bodies. J. Comput. Neurosci. 6, 169–184 (1999).

    CAS  PubMed  Google Scholar 

  55. Vargas-Irwin, C. E. et al. Decoding complete reach and grasp actions from local primary motor cortex populations. J. Neurosci. 30, 9659–9669 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Vaskov, A. K. et al. Cortical decoding of individual finger group motions using ReFIT Kalman filter. Front. Neurosci. 12, 751 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high-performance neural prosthesis incorporating discrete state selection with hidden markov models. IEEE Trans. Biomed. Eng. 64, 935–945 (2017).

    PubMed  Google Scholar 

  58. Barrese, J. C. et al. Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates. J. Neural Eng. 10, 066014 (2013).

    PubMed  PubMed Central  Google Scholar 

  59. Willett, F. R. et al. Feedback control policies employed by people using intracortical brain–computer interfaces. J. Neural Eng. 14, 016001 (2016).

    PubMed  PubMed Central  Google Scholar 

  60. Jarosiewicz, B. et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain–computer interface. Sci. Transl. Med. 7, 313ra179 (2015).

    PubMed  PubMed Central  Google Scholar 

  61. Novak, P. et al. Localization of the subthalamic nucleus in Parkinson disease using multiunit activity. J. Neurol. Sci. 310, 44–49 (2011).

    PubMed  PubMed Central  Google Scholar 

  62. Chestek, C. A. et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J. Neural Eng. 8, 045005 (2011).

    PubMed  PubMed Central  Google Scholar 

  63. Downey, J. E., Schwed, N., Chase, S. M., Schwartz, A. B. & Collinger, J. L. Intracortical recording stability in human brain–computer interface users. J. Neural Eng. 15, 046016 (2018).

    PubMed  Google Scholar 

  64. Yin, M. et al. An externally head-mounted wireless neural recording device for laboratory animal research and possible human clinical use. In Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 3109–3114 (IEEE, 2013).

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  67. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Lee, S. et al. A 330 μm × 90 μm opto-electronically integrated wireless system-on-chip for recording of neural activities. In IEEE Int. Solid State Circuits Conf. (ISSCC) 292–294 (IEEE, 2018).

  69. Robinet, S. et al. A low-power 0.7 μV rms 32-channel mixed-signal circuit for ECoG recordings. IEEE J. Emerg. Sel. Top. Circuits Syst. 1, 451–460 (2011).

    Google Scholar 

  70. Mestais, C. S. et al. WIMAGINE: wireless 64-channel ECoG recording implant for long term clinical applications. IEEE Trans. Neural Syst. Rehabilitation Eng. 23, 10–21 (2015).

    Google Scholar 

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

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

    PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  74. Slutzky, M. W. et al. Optimal spacing of surface electrode arrays for brain machine interface applications. J. Neural Eng. 7, 026004 (2010).

    Google Scholar 

  75. Davoodi, R., Urata, C., Hauschild, M., Khachani, M. & Loeb, G. E. Model-based development of neural prostheses for movement. IEEE Trans. Biomed. Eng. 54, 1909–1918 (2007).

    PubMed  Google Scholar 

  76. Wahnoun, R., He, J. & HelmsTillery, S. I. Selection and parameterization of cortical neurons for neuroprosthetic control. J. Neural Eng. 3, 162–171 (2006).

    PubMed  Google Scholar 

  77. Thompson, D. E. et al. Performance measurement for brain-computer or brain–machine interfaces: a tutorial. J. Neural Eng. 11, 035001 (2014).

    PubMed  PubMed Central  Google Scholar 

  78. Aggarwal, V. et al. Asynchronous decoding of dexterous finger movements using M1 neurons. IEEE Trans. Neural Syst. Rehabilitation Eng. 16, 3–14 (2008).

    Google Scholar 

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Acknowledgements

We thank E. Kennedy for animal and experimental support. We thank G. Rising, A. Yanovich, L. Burlingame, P. Lester, V. Dunivant, L. Durham, T. Hetrick, H. Noack, D. Renner, M. Bradley, G. Chan, K. Cornelius, C. Hunter, L. Krueger, R. Nichols, B. Pallas, C. Si, A. Skorupski, J. Xu, J. Yang, M. Risch, M. Wechsler and R. Reeder for expert surgical assistance and veterinary care. We thank B. Davis for administrative assistance. We thank W. L. Gore Inc. for donating Preclude artificial dura, used as part of some of the chronic electrode array implantation procedures, and S. Ryu for performing array implantation surgeries. This work was supported by NSF grant no. 1926576, Craig H. Neilsen Foundation project 315108, A. Alfred Taubman Medical Research Institute, NIH grant no. R01GM111293, MCubed project 1482 and NIH grant no. R21EY029452. S.R.N. was supported by NIH grant no. F31HD098804. A.K.V. was supported by fellowship from the Robotics Graduate Program at University of Michigan. M.S.W. was supported by NIH grant no. T32NS007222. E.J.W. was supported by NIH grant nos. U01NS094375 and UF1NS107659, and Office of the Director National Institutes of Health OT2OD024907. H.A., T.J., H.-S.K. and D.B. were supported by MCubed project 1482 and NIH grant no. R21EY029452. P.P.V., A.J.B., C.S.N. and J.C.K. were supported by NSF-GRFP. K.V.S. was supported in part by the following awards: NIH National Institute of Neurological Disorders and Stroke Transformative Research Award R01NS076460, NIH National Institute of Mental Health Transformative Research Award R01MH09964703, NIH Director’s Pioneer Award 8DP1HD075623, Defense Advanced Research Projects Agency (DARPA) Biological Technology Office (BTO) ‘REPAIR’ Award N66001-10-C-2010, DARPA BTO ‘NeuroFAST’ Award W911NF-14-2-0013, Simons Foundation Collaboration on the Global Brain award 543045, the Office of Naval Research W911NF-14-2-0013 and the Howard Hughes Medical Institute. P.G.P. was supported by NSF grant no. 1926576, A. Alfred Taubman Medical Research Institute and NIH grant no. R01GM111293. C.A.C. was supported by NSF grant no. 1926576, Craig H. Neilsen Foundation project 315108, NIH grant nos. R01GM111293 and R21EY029452, and MCubed project 1482.

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M.S.W., K.V.S., P.G.P. and C.A.C. supervised this work and conducted non-human primate surgeries. H.A., T.J., H.-S.K. and D.B. designed and estimated power consumption of the integrated circuits and wrote the relevant text. J.C.K. and K.V.S. conducted and supplied two-dimensional arm reaching experiments and data. A.K.V., P.P.V., A.J.B. and C.S.N. assisted with non-human primate experiments and simulation programming. E.J.W. conducted rat experiments. S.R.N. programmed and executed all simulations, decoding experiments and data analysis, and wrote the manuscript. All authors reviewed and modified the manuscript.

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Correspondence to Cynthia A. Chestek.

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K.V.S. is a consultant for Neuralink Corp. and is on the scientific advisory board for CTRL-Labs Inc., MIND-X Inc., Inscopix Inc. and Heal Inc. These entities did not provide support for this work.

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Supplementary Video 1

Index-finger control in monkey W.

Supplementary Video 2

Control of the middle/ring/small finger in monkey N.

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Nason, S.R., Vaskov, A.K., Willsey, M.S. et al. A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces. Nat Biomed Eng 4, 973–983 (2020). https://doi.org/10.1038/s41551-020-0591-0

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