Advertisement

Challenges in the Design of Large-Scale, High-Density, Wireless Stimulation and Recording Interface

  • Po-Min Wang
  • Stanislav Culaclii
  • Kyung Jin Seo
  • Yushan Wang
  • Hui Fang
  • Yi-Kai Lo
  • Wentai LiuEmail author
Chapter
  • 5 Downloads

Abstract

The need to better understand the nervous system drives the technological advancements in the development of neural stimulation and recording interface. In spite of rapid technological evolution, there are still challenges to realize a large-scale, high-density neural interface. In this chapter, we first discuss the design challenges from the system to the components level for a high-density wireless stimulation and recording system. State-of-the-art technologies for the critical functional blocks in the system, including ultrahigh-data-rate wireless link, suppression of the stimulation artifact, focalized stimulation scheme, and high-density electrode array are also reviewed. At the end of this chapter, a large-scale, high-density wireless stimulation and recording system that integrates those critical components are presented.

Keywords

Wireless neural recording Neural stimulation Stimulation artifact cancellation High-density electrode array Focalized stimulation 

Notes

Acknowledgments

Seo, K. J. and Fang, H. acknowledge support of this work by the National Science Foundation (NSF CAREER, ECCS-1847215), the National Institutes of Health (NIH R21EY030710) and the Samsung Global Research Outreach (GRO) program.

References

  1. 1.
    Khodagholy, D., Gelinas, J. N., Thesen, T., Doyle, W., Devinsky, O., Malliaras, G. G., & Buzsáki, G. (2015). NeuroGrid: Recording action potentials from the surface of the brain. Nature Neuroscience, 18(2), 310.CrossRefGoogle Scholar
  2. 2.
    Viventi, J., Kim, D.-H., Vigeland, L., Frechette, E. S., Blanco, J. A., Kim, Y.-S., Avrin, A. E., Tiruvadi, V. R., Hwang, S.-W., Vanleer, A. C., Wulsin, D. F., Davis, K., Gelber, C. E., Palmer, L., Van der Spiegel, J., Wu, J., Xiao, J., Huang, Y., Contreras, D., Rogers, J. A., & Litt, B. (2011). Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nature Neuroscience, 14(12), 1599.Google Scholar
  3. 3.
    Yin, M., Borton, D. A., Komar, J., Agha, N., Lu, Y., Li, H., Laurens, J., Lang, Y., Li, Q., Bull, C., Larson, L., Rosler, D., Bezard, E., Courtine, G., & Nurmikko, A. V. (2014). Wireless neurosensor for full-spectrum electrophysiology recordings during free behavior. Neuron, 84(6), 1170–1182.Google Scholar
  4. 4.
    Lo, Y.-K., Kuan, Y.-C., Culaclii, S., Kim, B., Wang, P.-M., Chang, C.-W., Massachi, J. A., Zhu, M., Chen, K., Gad, P., Edgerton, V. R., & Liu, W. (2017). A fully integrated wireless SoC for motor function recovery after spinal cord injury. IEEE Transactions on Biomedical Circuits and Systems, 11(3), 497–509.Google Scholar
  5. 5.
    Shahdoost, S., Frost, S., Guggenmos, D., Borrell, J., Dunham, C., Barbay, S., Nudo, R., & Mohseni, P. (2016). A miniaturized brain-machine-spinal cord interface (BMSI) for closed-loop intraspinal microstimulation. In: IEEE biomedical circuits and systems conference (BioCAS), Shanghai, China (pp. 364–367).Google Scholar
  6. 6.
    Liu, W., Wang, P.-M., & Lo, Y.-K. (2017). Towards closed-loop neuromodulation: A wireless miniaturized neural implant SoC. In: SPIE defense+ security, International Society for Optics and Photonics, Anaheim, California, USA (pp. 1019414–1019418).Google Scholar
  7. 7.
    Lo, Y.-K., Wang, P.-M., Dubrovsky, G., Wu, M.-D., Chan, M., Dunn, J. C., & Liu, W. (2018). A wireless implant for gastrointestinal motility disorders. Micromachines, 9(1), 17.CrossRefGoogle Scholar
  8. 8.
    Arriagada, A., Jurkov, A., Neshev, E., Muench, G., Andrews, C., & Mintchev, M. (2011). Design, implementation and testing of an implantable impedance-based feedback-controlled neural gastric stimulator. Physiological Measurement, 32(8), 1103.CrossRefGoogle Scholar
  9. 9.
    Deb, S., Tang, S.-J., Abell, T. L., McLawhorn, T., Huang, W.-D., Lahr, C., To, S. F., Easter, J., & Chiao, J.-C. (2012). Development of innovative techniques for the endoscopic implantation and securing of a novel, wireless, miniature gastrostimulator (with videos). Gastrointestinal Endoscopy, 76(1), 179–184.CrossRefGoogle Scholar
  10. 10.
    Lo, Y.-K., Chen, K., Gad, P., & Liu, W. (2013). A fully-integrated high-compliance voltage SoC for epi-retinal and neural prostheses. IEEE Transactions on Biomedical Circuits and Systems, 7(6), 761–772.CrossRefGoogle Scholar
  11. 11.
    Noorsal, E., Sooksood, K., Xu, H., Hornig, R., Becker, J., & Ortmanns, M. (2012). A neural stimulator frontend with high-voltage compliance and programmable pulse shape for epiretinal implants. IEEE Journal of Solid-State Circuits, 47(1), 244–256.CrossRefGoogle Scholar
  12. 12.
    Monge, M., Raj, M., Honarvar-Nazari, M., Chang, H.-C., Zhao, Y., Weiland, J., Humayun, M., Tai, Y-C., & Emami-Neyestanak, A. (2013). A fully intraocular 0.0169 mm 2/pixel 512-channel self-calibrating epiretinal prosthesis in 65nm CMOS. In: IEEE international solid-state circuits conference digest of technical papers (ISSCC), San Francisco, California, USA (pp. 296–297).Google Scholar
  13. 13.
    Stanslaski, S., Afshar, P., Cong, P., Giftakis, J., Stypulkowski, P., Carlson, D., Linde, D., Ullestad, D., Avestruz, A.-T., & Denison, T. (2012). Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), 410–421.CrossRefGoogle Scholar
  14. 14.
    Sekirnjak, C., Hottowy, P., Sher, A., Dabrowski, W., Litke, A. M., & Chichilnisky, E. (2008). High-resolution electrical stimulation of primate retina for epiretinal implant design. Journal of Neuroscience, 28(17), 4446–4456.CrossRefGoogle Scholar
  15. 15.
    Yang, Z., Xu, J., Nguyen, A. T., Wu, T., Zhao, W., & Tam, W.-K. (2016). Neuronix enables continuous, simultaneous neural recording and electrical microstimulation. In: IEEE 38th annual international conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA (pp. 4451–4454).Google Scholar
  16. 16.
    Chandrakumar, H., & Marković, D. (2017). A high dynamic-range neural recording chopper amplifier for simultaneous neural recording and stimulation. IEEE Journal of Solid-State Circuits, 52(3), 645–656.CrossRefGoogle Scholar
  17. 17.
    Hashimoto, T., Elder, C. M., & Vitek, J. L. (2002). A template subtraction method for stimulus artifact removal in high-frequency deep brain stimulation. Journal of Neuroscience Methods, 113(2), 181–186.CrossRefGoogle Scholar
  18. 18.
    Wichmann, T. (2000). A digital averaging method for removal of stimulus artifacts in neurophysiologic experiments. Journal of Neuroscience Methods, 98(1), 57–62.CrossRefGoogle Scholar
  19. 19.
    Wagenaar, D. A., & Potter, S. M. (2002). Real-time multi-channel stimulus artifact suppression by local curve fitting. Journal of Neuroscience Methods, 120(2), 113–120.CrossRefGoogle Scholar
  20. 20.
    Mena, G. E., Grosberg, L. E., Madugula, S., Hottowy, P., Litke, A., Cunningham, J., Chichilnisky, E., & Paninski, L. (2017). Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Computational Biology, 13(11), e1005842.CrossRefGoogle Scholar
  21. 21.
    Yochum, M., & Binczak, S. (2015). A wavelet based method for electrical stimulation artifacts removal in electromyogram. Biomedical Signal Processing and Control, 22, 1–10.CrossRefGoogle Scholar
  22. 22.
    Allen, D. P., Stegemöller, E. L., Zadikoff, C., Rosenow, J. M., & MacKinnon, C. D. (2010). Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering. Clinical Neurophysiology, 121(8), 1227–1232.CrossRefGoogle Scholar
  23. 23.
    O’Shea, D. J., & Shenoy, K. V. (2018). ERAASR: An algorithm for removing electrical stimulation artifacts from multielectrode array recordings. Journal of Neural Engineering, 15(2), 026020.CrossRefGoogle Scholar
  24. 24.
    Zhou, A., Santacruz, S. R., Johnson, B. C., Alexandrov, G., Moin, A., Burghardt, F. L., Rabaey, J. M., Carmena, J. M., & Muller, R. (2017). WAND: A 128-channel, closed-loop, wireless artifact-free neuromodulation device. arXiv preprint arXiv:170800556.Google Scholar
  25. 25.
    Culaclii, S., Kim, B., Lo, Y.-K., Li, L., & Liu, W. (2018). Online artifact cancelation in same-electrode neural stimulation and recording using a combined hardware and software architecture. IEEE Transactions on Biomedical Circuits and Systems, 12(3), 601–613.CrossRefGoogle Scholar
  26. 26.
    Fernandez-Corazza, M., Turovets, S., Luu, P., Anderson, E., & Tucker, D. (2016). Transcranial electrical neuromodulation based on the reciprocity principle. Frontiers in Psychiatry, 7, 87.CrossRefGoogle Scholar
  27. 27.
    Sui, Y., & Burdick, J. (2014). Clinical online recommendation with subgroup rank feedback. In: Proceedings of the 8th ACM conference on recommender systems, Foster City, California, USA (pp. 289–292).Google Scholar
  28. 28.
    Dmochowski, J. P., Datta, A., Bikson, M., Su, Y., & Parra, L. C. (2011). Optimized multi-electrode stimulation increases focality and intensity at target. Journal of Neural Engineering, 8(4), 046011.CrossRefGoogle Scholar
  29. 29.
    Sadleir, R., Vannorsdall, T. D., Schretlen, D. J., & Gordon, B. (2012). Target optimization in transcranial direct current stimulation. Frontiers in Psychiatry, 3, 90.CrossRefGoogle Scholar
  30. 30.
    Huang, Y., Thomas, C., Datta, A., & Parra, L. C. (2018). Optimized tDCS for targeting multiple brain regions: An integrated implementation. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018, 3545.Google Scholar
  31. 31.
    Anderson, D. N., Osting, B., Vorwerk, J., Dorval, A. D., & Butson, C. R. (2018). Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. Journal of Neural Engineering, 15(2), 026005.CrossRefGoogle Scholar
  32. 32.
    Feng, X.-J., Greenwald, B., Rabitz, H., Shea-Brown, E., & Kosut, R. (2007). Toward closed-loop optimization of deep brain stimulation for Parkinson’s disease: Concepts and lessons from a computational model. Journal of Neural Engineering, 4(2), L14.CrossRefGoogle Scholar
  33. 33.
    Alo, R., Alo, K., Ilochonwu, O., Kreinovich, V., & Nguyen, H. P. (1998). Towards optimal pain relief: Acupuncture and spinal cord stimulation. In: Proceedings of the 2nd International Workshop on Intelligent Virtual Environments, Xalapa, Veracruz, Mexico (pp. 16–24).Google Scholar
  34. 34.
    Herculano-Houzel, S. (2009). The human brain in numbers: A linearly scaled-up primate brain. Frontiers in Human Neuroscience, 3, 31.CrossRefGoogle Scholar
  35. 35.
    Carmena, J. M. (2013). Advances in neuroprosthetic learning and control. PLoS Biology, 11(5), e1001561.MathSciNetCrossRefGoogle Scholar
  36. 36.
    Stevenson, I. H., & Kording, K. P. (2011). How advances in neural recording affect data analysis. Nature Neuroscience, 14(2), 139–142.CrossRefGoogle Scholar
  37. 37.
    Fang, H., Yu, K. J., Gloschat, C., Yang, Z., Song, E., Chiang, C.-H., Zhao, J., Won, S. M., Xu, S., Trumpis, M., Zhong, Y., Han, S. W., Xue, Y., Xu, D., Choi, S. W., Cauwenberghs, G., Kay, M., Huang, Y., Viventi, J., Efimov, I. R. & Rogers, J. A. (2017). Capacitively coupled arrays of multiplexed flexible silicon transistors for long-term cardiac electrophysiology. Nature Biomedical Engineering, 1(3), 0038.Google Scholar
  38. 38.
    Viventi, J., Kim, D.-H., Moss, J. D., Kim, Y.-S., Blanco, J. A., Annetta, N., Hicks, A., Xiao, J., Huang, Y., Callans, D. J., Rogers, J. A., & Litt, B. (2010). A conformal, bio-interfaced class of silicon electronics for mapping cardiac electrophysiology. Science Translational Medicine, 2(24), 24ra22–24ra22.Google Scholar
  39. 39.
    Du, J., Blanche, T. J., Harrison, R. R., Lester, H. A., & Masmanidis, S. C. (2011). Multiplexed, high density electrophysiology with nanofabricated neural probes. PLoS One, 6(10), e26204.CrossRefGoogle Scholar
  40. 40.
    Yu, K. J., Kuzum, D., Hwang, S.-W., Kim, B. H., Juul, H., Kim, N. H., Won, S. M., Chiang, K., Trumpis, M., Richardson, A. G., Cheng, H., Fang H., Thompson, M., Bink, H., Talos, D., Seo, K. J., Lee, H. N., Kang, S.-K., Kim, J.-H., Lee, J. Y., Huang, Y., Jensen, F. E., Dichter, M. A., Lucas, T. H., Viventi, J., Litt, B., & Rogers, J. A. (2016). Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex. Nature Materials, 15(7), 782.Google Scholar
  41. 41.
    Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., Lee, A. K., Anastassiou, C. A., Andrei, A., Aydın, Ç., Barbic, M., Blanche, T. J., Bonin, V., Couto, J., Dutta, B., Gratiy, S. L., Gutnisky, D. A., Häusser, M., Karsh, B., Ledochowitsch, P., Lopez, C. M., Mitelut, C., Musa, S., Okun, M., Pachitariu, M., Putzeys, J., Rich, P.D., Rossant, C., Sun, W.-L., Svoboda, K., Carandini, M., Harris, K. D., Koch, C., O’Keefe, J., & Harris, T. D. (2017). Fully integrated silicon probes for high-density recording of neural activity. Nature, 551(7679), 232.Google Scholar
  42. 42.
    Raducanu, B. C., Yazicioglu, R. F., Lopez, C. M., Ballini, M., Putzeys, J., Wang, S., Andrei, A., Rochus, V., Welkenhuysen, M., Helleputte, N. V., Musa, S., Puers, R., Kloosterman, F., Van Hoof, C., Fiáth, R., Ulbert, I., & Mitra, S.  (2017). Time multiplexed active neural probe with 1356 parallel recording sites. Sensors, 17(10), 2388.Google Scholar
  43. 43.
    Fang, H., Zhao, J., Yu, K. J., Song, E., Farimani, A. B., Chiang, C.-H., Jin, X., Xue, Y., Xu, D., Du, W., Seo K. J., Zhong, Y., Yang, Z., Won, S. M., Fang, G., Choi, S. W., Chaudhuri, S., Huang, Y., Alam, M. A., Viventi, J., Aluru, N. R., & Rogers, J. A. (2016). Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems. Proceedings of the National Academy of Sciences, 113(42), 11682–11687.Google Scholar
  44. 44.
    Berényi, A., Somogyvári, Z., Nagy, A. J., Roux, L., Long, J. D., Fujisawa, S., Stark, E., Leonardo, A., Harris, T. D., & Buzsáki, G. (2013). Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals. Journal of Neurophysiology, 111(5), 1132–1149.CrossRefGoogle Scholar
  45. 45.
    Schwarz, D. A., Lebedev, M. A., Hanson, T. L., Dimitrov, D. F., Lehew, G., Meloy, J., Rajangam, S., Subramanian, V., Ifft, P. J., Li, Z., Ramakrishnan, A., Tate, A., Zhuang K. Z. & Nicolelis M. A. L. (2014). Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nature Methods, 11(6), 670.Google Scholar
  46. 46.
    Shobe, J. L., Claar, L. D., Parhami, S., Bakhurin, K. I., & Masmanidis, S. C. (2015). Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes. Journal of Neurophysiology, 114(3), 2043–2052.CrossRefGoogle Scholar
  47. 47.
    Rajangam, S., Tseng, P.-H., Yin, A., Lehew, G., Schwarz, D., Lebedev, M. A., & Nicolelis, M. A. (2016). Wireless cortical brain-machine interface for whole-body navigation in primates. Scientific Reports, 6, 22170.CrossRefGoogle Scholar
  48. 48.
    Harrison, R. R. (2007). A versatile integrated circuit for the acquisition of biopotentials. In: IEEE custom integrated circuits conference, San Jose, California, USA (pp. 115–122).Google Scholar
  49. 49.
    Liu, X., Zhang, M., Xiong, T., Richardson, A. G., Lucas, T. H., Chin, P. S., Etienne-Cummings, R., Tran, T. D., & Van der Spiegel, J. (2016). A fully integrated wireless compressed sensing neural signal acquisition system for chronic recording and brain machine interface. IEEE Transactions on Biomedical Circuits and Systems, 10(4), 874–883.CrossRefGoogle Scholar
  50. 50.
    Zhang, J., Suo, Y., Mitra, S., Chin, S. P., Hsiao, S., Yazicioglu, R. F., Tran, T. D., & Etienne-Cummings, R. (2014). An efficient and compact compressed sensing microsystem for implantable neural recordings. IEEE Transactions on Biomedical Circuits and Systems, 8(4), 485–496.CrossRefGoogle Scholar
  51. 51.
    Kim, S., Tathireddy, P., Normann, R. A., & Solzbacher, F. (2007). Thermal impact of an active 3-D microelectrode array implanted in the brain. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(4), 493–501.CrossRefGoogle Scholar
  52. 52.
    Silay, K. M., Dehollain, C., & Declercq, M. (2008). Numerical analysis of temperature elevation in the head due to power dissipation in a cortical implant. In: IEEE 30th annual international conference of the Engineering in Medicine and Biology Society, (EMBS), Vancouver, British Columbia, Canada (pp. 951–956).Google Scholar
  53. 53.
    Lazzi, G. (2005). Thermal effects of bioimplants. IEEE Engineering in Medicine and Biology Magazine, 24(5), 75–81.CrossRefGoogle Scholar
  54. 54.
    Ibrahim, T. S., Abraham, D., & Rennaker, R. L. (2007). Electromagnetic power absorption and temperature changes due to brain machine interface operation. Annals of Biomedical Engineering, 35(5), 825–834.CrossRefGoogle Scholar
  55. 55.
    Chen, F., Chandrakasan, A. P., & Stojanovic, V. M. (2012). Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors. IEEE Journal of Solid-State Circuits, 47(3), 744–756.CrossRefGoogle Scholar
  56. 56.
    Kuan, Y.-C., Lo, Y.-K., Kim, Y., Chang, M.-C. F., & Liu, W. (2015). Wireless gigabit data telemetry for large-scale neural recording. IEEE Journal of Biomedical and Health Informatics, 19(3), 949–957.Google Scholar
  57. 57.
    Rezaei, M., Bahrami, H., Mirbozorgi, A., Rusch, L. A., & Gosselin, B. (2016). A short-impulse UWB BPSK transmitter for large-scale neural recording implants. In: IEEE 38th annual international conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA (pp. 6315–6318).Google Scholar
  58. 58.
    Mirbozorgi, S. A., Bahrami, H., Sawan, M., Rusch, L. A., & Gosselin, B. (2016). A single-chip full-duplex high speed transceiver for multi-site stimulating and recording neural implants. IEEE Transactions on Biomedical Circuits and Systems, 10(3), 643–653.CrossRefGoogle Scholar
  59. 59.
    Elzeftawi, M., & Theogarajan, L. (2013). A 10pJ/bit 135Mbps IR-UWB transmitter using pulse position modulation and with on-chip LDO regulator in 0.13 μm CMOS for biomedical implants. In: 2013 IEEE topical conference on biomedical wireless technologies, networks, and sensing systems (BioWireleSS), Austin, Texas, USA (pp. 37–39).Google Scholar
  60. 60.
    Crepaldi, M., Angotzi, G. N., Maviglia, A., Diotalevi, F., & Berdondini, L. (2018). A 5 pJ/pulse at 1-Gpps pulsed transmitter based on asynchronous logic master–slave PLL synthesis. IEEE Transactions on Circuits and Systems I: Regular Papers, 65(3), 1096–1109.CrossRefGoogle Scholar
  61. 61.
    Chahat, N., Zhadobov, M., Le Coq, L., Alekseev, S. I., & Sauleau, R. (2012). Characterization of the interactions between a 60-GHz antenna and the human body in an off-body scenario. IEEE Transactions on Antennas and Propagation, 60(12), 5958–5965.CrossRefGoogle Scholar
  62. 62.
    Zhadobov, M., Nicolaz, C. N., Sauleau, R., Desmots, F., Thouroude, D., Michel, D., & Le Dréan, Y. (2009). Evaluation of the potential biological effects of the 60-GHz millimeter waves upon human cells. IEEE Transactions on Antennas and Propagation, 57(10), 2949–2956.CrossRefGoogle Scholar
  63. 63.
    Feynman, R. P., Leighton, R. B., & Sands, M. (2005). The Feynman lectures on physics including Feynman’s tips on physics: The definitive and extended edition. Reading: Addison Wesley.Google Scholar
  64. 64.
    Zou, Z. (2011). Impulse radio UWB for the internet-of-things: A study on UHF/UWB hybrid solution. Doctoral dissertation, KTH Royal Institute of Technology.Google Scholar
  65. 65.
    Chen, K., Yang, Z., Hoang, L., Weiland, J., Humayun, M., & Liu, W. (2010). An integrated 256-channel epiretinal prosthesis. IEEE Journal of Solid-State Circuits, 45(9), 1946–1956.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Po-Min Wang
    • 1
  • Stanislav Culaclii
    • 1
  • Kyung Jin Seo
    • 2
  • Yushan Wang
    • 1
  • Hui Fang
    • 2
  • Yi-Kai Lo
    • 3
  • Wentai Liu
    • 1
    Email author
  1. 1.Department of BioengineeringUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Department of Electrical and Computer EngineeringNortheastern UniversityBostonUSA
  3. 3.Niche Biomedical Inc.Los AngelesUSA

Personalised recommendations