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Towards a Wireless Implantable Brain-Machine Interface for Locomotion Control

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Handbook of Neuroengineering

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Abstract

Implantable brain-machine interface (BMI) technology provides a promising solution for restoring independent locomotion for people with tetraplegia. However, current BMI systems used in clinical trials have not been widely adopted due to a number of shortcomings, one of which is the need for a wired connection. In this chapter, we present an example of a wireless BMI system implemented on a macaque model, showing that animals were able to use wirelessly transmitted neural signals for self-driving. From this example we discuss different aspects of design for such a BMI system, including data acquisition, signal processing, decoding/control algorithms, and neural responses when using the system. Future developments in this research field will enable BMI for locomotion control to become a reality.

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References

  1. Aflalo, T., Kellis, S., Klaes, C., Lee, B., Shi, Y., Pejsa, K., Shanfield, K., Hayes-Jackson, S., Aisen, M., Heck, C., Liu, C., Andersen, R.: Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science. 348, 906–910 (2015)

    Google Scholar 

  2. Ajiboye, A.B., Willett, F.R., Young, D.R., Memberg, W.D., Murphy, B.A., Miller, J.P., Walter, B.L., Sweet, J.A., Hoyen, H.A., Keith, M.W., Peckham, P.H., Simeral, J.D., Donoghue, J.P., Hochberg, L.R., Kirsch, R.F.: 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)

    Google Scholar 

  3. Barrese, J.C., Aceros, J., Donoghue, J.P.: Scanning electron microscopy of chronically implanted intracortical microelectrode arrays in non-human primates. J. Neural Eng. 13(2), 026003 (2016)

    Google Scholar 

  4. Benabid, A.L., Costecalde, T., Eliseyev, A., Charvet, G., Verney, A., Karakas, S., Foerster, M., Lambert, A., Morinière, B., Abroug, N., Schaeffer, M.C., Moly, A., Sauter-Starace, F., Ratel, D., Moro, C., Torres-Martinez, N., Langar, L., Oddoux, M., Polosan, M., Pezzani, S., Auboiroux, V., Aksenova, T., Mestais, C., Chabardes, S.: An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol. 18(12), 1112–1122 (2019)

    Google Scholar 

  5. Boi, F., Moraitis, T., Feo, V., Diotalevi, F., Bartolozzi, C., Indiveri, G., Vato, A.: A bidirectional brain-machine interface featuring a neuromorphic hardware decoder. Front. Neurosci., 1–15 (2016)

    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(2) (2013)

    Google Scholar 

  7. Bouton, C.E., Shaikhouni, A., Annetta, N.V., Bockbrader, M.A., Friedenberg, D.A., Nielson, D.M., Sharma, G., Sederberg, P.B., Glenn, B.C., Mysiw, W.J., Morgan, A.G., Deogaonkar, M., Rezai, A.R.: Restoring cortical control of functional movement in a human with quadriplegia. Nature. 533, 247–250 (2016)

    Google Scholar 

  8. Brandman, D.M., Hosman, T., Saab, J., Burkhart, M.C., Shanahan, B.E., Ciancibello, J.G., Sarma, A.A., Milstein, D.J., Vargas-Irwin, C.E., Hochberg, L.R., et al.: Rapid calibration of an intracortical brain–computer interface for people with tetraplegia. J. Neural Eng. 15(2), 026007 (2018)

    Google Scholar 

  9. Carlson, D., Carin, L.: Continuing progress of spike sorting in the era of big data. Curr. Opin. Neurobiol. 55, 90–96 (2019)

    Google Scholar 

  10. Carlson, T., Millán, J.R.: Brain-controlled wheelchairs: a robotic architecture. IEEE Robot. Autom. Mag. (2013)

    Google Scholar 

  11. Carmena, J.M., Lebedev, M.A., Crist, R.E., O’Doherty, J.E., Santucci, D.M., Dimitrov, D.F., Patil, P.G., Henriquez, C.S., Nicolelis, M.A.L.: Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, E42 (2003)

    Google Scholar 

  12. 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(3), 679–692 (2016)

    Google Scholar 

  13. Cherry, M.S., Kota, S., Young, A., Ferris, D.P.: Running with an elastic lower limb exoskeleton. J. Appl. Biomech. 32(3), 269–277 (2016)

    Google Scholar 

  14. Chestek, C.A., Gilja, V., Nuyujukian, P., Foster, J.D., Fan, J.M., Kaufman, M.T., Churchland, M.M., Rivera-Alvidrez, Z., Cunningham, J.P., RyuI, S.I., Shenoy, K.V.: Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J. Neural Eng. 8, 045005 (2011)

    Google Scholar 

  15. Christie, B.P., Tat, D.M., Irwin, Z.T., Gilja, V., Nuyujukian, P., Foster, J.D., Ryu, S.I., Shenoy, K.V., Thompson, D.E., Chestek, C.A.: Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance. J. Neural Eng. 12(1), 016009 (2015)

    Google Scholar 

  16. Collinger, J.L., Wodlinger, B., Downey, J.E., Wang, W., Tyler-Kabara, E.C., Weber, D.J., McMorland, A.J.C., Velliste, M., Boninger, M.L., Schwartz, A.B.: High-performance neuroprosthetic control by an individual with tetraplegia. Lancet. 381, 557–564 (2013)

    Google Scholar 

  17. Dai, J., Zhang, P., Sun, H., Qiao, X., Zhao, Y., Ma, J., Li, S., Zhou, J., Wang, C.: Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain-machine interface. J. Neural Eng. 16(3), 036011 (2019)

    Google Scholar 

  18. Dethier, J., Nuyujukian, P., Ryu, S.I., Shenoy, K.V., Boahen, K.: Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces. J. Neural Eng. 10(3) (2013)

    Google Scholar 

  19. Esquenazi, A., Talaty, M.: Robotics for lower limb rehabilitation. Phys. Med. Rehabil. Clin. N. Am. 30(2), 385–397 (2019)

    Google Scholar 

  20. Ethier, C., Oby, E.R., Bauman, M.J., Miller, L.E.: Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature. 485(7398) (2012)

    Google Scholar 

  21. 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(5), 056005 (2013)

    Google Scholar 

  22. Flint, R.D., Scheid, M.R., Wright, Z.A., Solla, S.A., Slutzky, M.W.: Long-term stability of motor cortical activity: implications for brain machine interfaces and optimal feedback control. J. Neurosci. 36(12), 3623–3632 (2016)

    Google Scholar 

  23. Fraser, G.W., Chase, S.M., Whitford, A., Schwartz, A.B.: Control of a brain-computer interface without spike sorting. J. Neural Eng. 6, 055004 (2009)

    Google Scholar 

  24. Ganguly, K., Dimitrov, D.F., Wallis, J.D., Carmena, J.M.: Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14(5), 662–667 (2011)

    Google Scholar 

  25. Gilja, V., Nuyujukian, P., Chestek, C.A., Cunningham, J.P., Yu, B.M., Fan, J.M., Churchland, M.M., Kaufman, M.T., Kao, J.C., Ryu, S.I., Shenoy, K.V.: A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15(12), 1752–1757 (2012)

    Google Scholar 

  26. Golub, M.D., Sadtler, P.T., Oby, E.R., Quick, K.M., Ryu, S.I., Tyler-Kabara, E.C., Batista, A.P., Chase, S.M., Yu, M.B.: Learning by neural reassociation. Nat. Neurosci. 21(4), 607 (2018)

    Google Scholar 

  27. He, Y., Eguren, D., Azorín, J.M., Grossman, R.G., Luu, T.P., Contreras-Vidal, J.L.: Brain-machine interfaces for controlling lower-limb powered robotic systems. J. Neural Eng. 15(2), 021004 (2018)

    Google Scholar 

  28. Hochberg, L.R., Serruya, M.D., Friehs, G.M., et al.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 442(7099), 164–171 (2006)

    Google Scholar 

  29. Hochberg, L.R., Bacher, D., Jarosiewicz, B., Masse, N.Y., Simeral, J.D., Vogel, J., Haddadin, S., Liu, J., Cash, S.S., van der Smagt, P., Donoghue, J.P.: Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 485(7398), 372–375 (2012)

    Google Scholar 

  30. Iturrate, I., Antelis, J.M., Kübler, A., Minguez, J.: A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans. Robot. 25, 614–627 (2009)

    Google Scholar 

  31. Jarosiewicz, B., Masse, N.Y., Bacher, D., Cash, S.S., Eskandar, E., Friehs, G., Donoghue, J.P., Hochberg, L.R.: Advantages of closed-loop calibration in intracortical brain-computer interfaces for people with tetraplegia. J. Neural Eng. 10(4), 046012 (2013)

    Google Scholar 

  32. Jun, J.J., Steinmetz, N.A., Siegle, J.H., Denman, D.J., Bauza, M., Barbarits, B., Lee, A.K., Aydin, C., Barbic, M.: Fully integrated silicon probes for high-density recording of neural activity. Nature. 551(7679), 232 (2017)

    Google Scholar 

  33. Kaiser, J.F.: On a simple algorithm to calculate the energy of a signal. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP-90), pp. 241–261 (1990)

    Google Scholar 

  34. Kim, S.-P., Simeral, J.D., Hochberg, L.R., Donoghue, J.P., Black, M.J.: Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5, 455–476 (2008)

    Google Scholar 

  35. Leeb, R., Friedman, D., Müller-Putz, G.R., Scherer, R., Slater, M., Pfurtscheller, G.: Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput. Intell. Neurosci. 2007, 8 (2007)

    Google Scholar 

  36. Liu, X., McCreery, D.B., Carter, R.R., Bullara, L.A., Yuen, T.G., Agnew, W.F.: Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes. IEEE Trans. Rehabil. Eng. 7, 315–326 (1999)

    Google Scholar 

  37. Milekovic, T., Sarma, A.A., Bacher, D., Simeral, J.D., Saab, J., Pandarinath, C., Sorice, B.L., Blabe, C., Oakley, E.M., Tringale, K.R., Eskandar, E., Cash, S.S., Henderson, J.M., Shenoy, K.V., Donoghue, J.P., Hochberg, L.R.: Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. J. Neurophysiol. 120(1), 343–360 (2018)

    Google Scholar 

  38. Moritz, C.T., Fetz, E.E.: Volitional control of single cortical neurons in a brain–machine interface. J. Neural Eng. 8(2) (2011)

    Google Scholar 

  39. Musk, E.: An integrated brain-machine interface platform with thousands of channels. BioRxiv. 703801 (2019)

    Google Scholar 

  40. 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 

  41. Oby, E.R., Golub, M.D., Hennig, J.A., Degenhart, A.D., Tyler-Kabara, E.C., Yu, M.B., Chase, S.M., Batista, A.P.: New neural activity patterns emerge with long-term learning. Proc. Natl. Acad. Sci., 201820296 (2019)

    Google Scholar 

  42. Quiroga, Q., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16, 1661–1687 (2004)

    MATH  Google Scholar 

  43. Rajangam, S., Tseng, P.H., Yin, A., Lehew, G., Schwarz, D., Lebedev, M.A., Nicolelis, M.A.: Wireless cortical brain-machine interface for whole-body navigation in primates. Sci. Rep. 6, 22170 (2016)

    Google Scholar 

  44. Rebsamen, B., Guan, C., Zhang, H., Wang, C., Teo, C., Ang, M.H., Burdet, E.: A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 590–598 (2010)

    Google Scholar 

  45. Rizk, M., Bossetti, C.A., Jochum, T.A., Callender, S.H., Nicolelis, M.A., Turner, D.A., Wolf, P.D.: A fully implantable 96-channel neural data acquisition system. J. Neural Eng. 6(2), 026002 (2009)

    Google Scholar 

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

    MATH  Google Scholar 

  47. Shaikh, S., Chen, Y., Basu, A., So, R.: Cortical motor intention decoding on an analog co-processor with fast training for non-stationary data. In: 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. IEEE (2017)

    Google Scholar 

  48. Shaikh, S., So, R., Libedinsky, C., Basu, A.: Experimental comparison of hardware-amenable spike detection algorithms for iBMIs. In: 9th International IEEE/EMBS Conference on Neural Engineering (NER) (2019)

    Google Scholar 

  49. Simeral, J.D., Kim, S.P., Black, M.J., Donoghue, J.P., Hochberg, L.R.: Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J. Neural Eng. 8, 25027 (2011)

    Google Scholar 

  50. 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(2), 026002 (2014)

    Google Scholar 

  51. So, R.Q., Libedinsky, C., Ang, K.K., Lim, W.C.C., Toe, K.K., Guan, C.: Adaptive decoding using local field potentials in a brain-machine interface. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 5721–5724 (2016)

    Google Scholar 

  52. Stark, E., Abeles, M.: Predicting movement from multiunit activity. J. Neurosci. 27(31), 8387–8394 (2007)

    Google Scholar 

  53. 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(3), 036009 (2015)

    Google Scholar 

  54. Štrbac, M., Belić, M., Isaković, M., Kojić, V., Bijelić, G., Popović, I., Radotić, M., Došen, S., Marković, M., Farina, D., Keller, T.: Integrated and flexible multichannel interface for electrotactile stimulation. J. Neural Eng. 13(4), 046014 (2016)

    Google Scholar 

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

    Google Scholar 

  56. Trautmann, E.M., Stavisky, S.D., Lahiri, S., Ames, K.C., Kaufman, M.T., O’Shea, D.J., Vyas, S., Sun, X., Ryu, S.I., Ganguli, S., Shenoy, K.V.: Accurate estimation of neural population dynamics without spike sorting. Neuron. (2019)

    Google Scholar 

  57. Vansteensel, M.J., Pels, E.G.M., Bleichner, M.G., Branco, M.P., Denison, T., Freudenburg, Z.V., Gosselaar, P., Leinders, S., Ottens, T.H., Van Den Boom, M.A., Van Rijen, P.C., Aarnoutse, E.J., Ramsey, N.F.: Fully implanted brain-computer interface in a locked-in patient with ALS. N. Engl. J. Med. 375(21), 2060–2066 (2016)

    Google Scholar 

  58. Velliste, M., Perel, S., Spalding, M.C., Whitford, A.S., Schwartz, A.B.: Cortical control of a prosthetic arm for self-feeding. Nature. 453(7198), 1098–1101 (2008)

    Google Scholar 

  59. Wang, Y., She, X., Liao, Y., Li, H., Zhang, Q., Zhang, S., Zheng, X., Principe, J.: Tracking neural modulation depth by dual sequential Monte Carlo estimation on point processes for brain–machine interfaces. IEEE Trans. Biomed. Eng. 63(8), 1728–1741 (2016)

    Google Scholar 

  60. Wu, C.H., Mao, H.F., Hu, J.S., Wang, T.Y., Tsai, Y.J., Hsu, W.L.: The effects of gait training using powered lower limb exoskeleton robot on individuals with complete spinal cord injury. J. Neuroeng. Rehabil. 15(1), 14 (2018)

    Google Scholar 

  61. Xiang, Z., Liu, J., Lee, C.: A flexible three-dimensional electrode mesh: an enabling technology for wireless brain-computer interface prostheses. Microsyst. Nanoeng. 23(2), 16012 (2016)

    Google Scholar 

  62. Yao, E., Chen, Y., Basu, A.: A 0.7 v, 40 nw compact, current-mode neural spike detector in 65 nm cmos. IEEE Trans. Biomed. Circ. Syst. 10, 309–318 (2016)

    Google Scholar 

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Authors and Affiliations

  1. Institute for Infocomm Research, Singapore, Singapore

    Rosa Q. So

  2. National University of Singapore, Singapore, Singapore

    Camilo Libedinsky

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  1. Rosa Q. So
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  2. Camilo Libedinsky
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Correspondence to Rosa Q. So .

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Editors and Affiliations

  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

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So, R.Q., Libedinsky, C. (2023). Towards a Wireless Implantable Brain-Machine Interface for Locomotion Control. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_125

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