Integrated Amplifier Architectures for Efficient Coupling to the Nervous System

  • Timothy Denison
  • Gregory Molnar
  • Reid R. Harrison

Abstract

Monitoring the electrical activity of multiple neurons in the brain could enable a wide range of scientific and clinical endeavors. An enabling technology for neural monitoring is the interface amplifier. Current amplifier research is focused on two paradigms of chronically sensing neural activity: one is the measurement of ‘spike’ signals from individual neurons to provide high-fidelity control signals for neuroprosthesis, while the other is the measurement of bandpower fluctuations from cell ensembles that convey general information like the intention to move. In both measurement techniques, efforts to merge neural recording arrays with integrated electronics have revealed significant circuit design challenges. For example, weak neural signals, on the order of tens of microvolts rms, must be amplified prior to analysis and are often co-located with frequencies dominated by 1/f and popcorn noise in CMOS technologies. To insure the highest fidelity measurement, micropower chopper stabilization is often required to provide immunity from this excess noise. Another difficulty is that strict power constraints place severe limitations on the signal processing, algorithms and telemetry capabilities available in a practical system. These constraints motivate the design of the interface amplifier as part of a total system–level solution. In particular, the system solutions we pursued are driven by the key neural signal of interest, and we use the characteristics of the neural code guide the partitioning of the signal chain. To illustrate the generality of this design philosophy, we discuss state-of-the-art design examples from a spike-based, single-cell system, and a field potential, ensemble neuronal measurement system, both intended for practical and robust neuroprosthesis applications.

Keywords

Corn Attenuation Expense Sorting Encapsulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. Pine, “Recording action potentials from cultured neurons with extracellular microcircuit electrodes,” J. Neurosci. Methods, vol. 2, pp. 19–31, 1980.CrossRefGoogle Scholar
  2. 2.
    A.C. Hoogerwerf and K.D. Wise, “A three-dimensional microelectrode array for chronic neural recording,” IEEE Trans. Biomed. Eng., vol. 41, pp. 1136–1146, Dec. 1994.Google Scholar
  3. 3.
    A.L. Owens, T.J. Denison, H. Versnel, M. Rebbert, M. Peckerar, and S.A. Shamma, “Multi-electrode array for measuring evoked potentials from the surface of ferret primary auditory cortex,” J. Neurosci. Methods, vol. 58, pp. 209–220, 1995.CrossRefGoogle Scholar
  4. 4.
    C.T. Nordhausen, E.M. Maynard, and R.A. Normann, “Single unit recording capabilities of a 100 microelectrode array,” Brain Research, vol. 726, pp. 129–140, 1996.Google Scholar
  5. 5.
    K. Najafi and K.D. Wise, “An implantable multielectrode array with on-chip signal processing,” IEEE JSSC., vol. 21, pp. 1035–1044, Dec. 1986.Google Scholar
  6. 6.
    K.D. Wise, D.J. Anderson, J.F. Hetke, D.R. Kipke, and K. Najafi, “Wireless implantable microsystems: high-density electronic interfaces to the nervous system,” Proc. IEEE, vol. 92, pp. 76–97, Jan. 2004.Google Scholar
  7. 7.
    R.H. Olsson III and K.D. Wise, “A three-dimensional neural recording microsystem with implantable data compression circuitry,” IEEE J. Solid-State Cir., vol. 40, pp. 2796–2804, Dec. 2005.Google Scholar
  8. 8.
    R.R. Harrison, P.T. Watkins, R.J. Kier, R.O. Lovejoy, D.J. Black, B. Greger, and F. Solzbacher, “A low-power integrated circuit for a wireless 100-electrode neural recording system,” IEEE J. Solid-State Cir.,vol. 42, pp. 123–133, Jan. 2007.Google Scholar
  9. 9.
    T.M. Seese, H. Harasaki, G.M. Saidel, and C.R. Davies, “Characterization of tissue morphology, angiogenesis, and temperature in the adaptive response of muscle tissue to chronic heating,” Lab. Investigation, vol. 78(12), pp. 1553–1562, 1998.Google Scholar
  10. 10.
    J.C. LaManna, K.A. McCracken, M. Patil, and O.J. Prohaska, “Stimulus-activated changes in brain tissue temperature in the anesthetized rat,” Metabolic Brain Disease, vol. 4, pp. 225–237, 1989.Google Scholar
  11. 11.
    A. Jackson, J. Mavoori, and E.E. Fetz, “Long-term motor cortex plasticity induced by an electronic neural implant,” Nature, vol. 444, pp. 56–60, 2006.Google Scholar
  12. 12.
    J.K. Chapin, K.A. Moxon, R.S. Markowitz, and M.A.L. Nicolelis, “Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex,” Nature Neurosci., vol. 2, pp. 664–670, 1999.Google Scholar
  13. 13.
    J. Wessberg, C.R. Stambaugh, J.D. Kralik, P.D. Beck, M. Laubach, J.K. Chapin, J. Kim, S.J. Biggs, M.A. Srinivasan, and M.A.L. Nicolelis, “Real-time prediction of hand trajectory by ensembles of cortical neurons in primates,” Nature, vol. 408, pp. 361–365, 2000.CrossRefGoogle Scholar
  14. 14.
    M.D. Serruya, N.G. Hatsopoulos, L. Paninski, M.R. Fellows, and J.P. Donoghue, “Instant neural control of a movement signal,” Nature, vol. 416, pp. 141–142, 2002.CrossRefGoogle Scholar
  15. 15.
    D.M. Taylor, S.I.H. Tillery, and A.B. Schwartz, “Direct cortical control of 3-D neuroprosthetic devices,” Science, vol. 296, pp. 1829–1832, 2002.CrossRefGoogle Scholar
  16. 16.
    P.R. Kennedy, R.A.E. Bakay, M.M. Moore, K. Adams, and J. Goldthwaite, “Direct control of a computer from the human central nervous system,” IEEE Trans. Rehab. Eng., vol. 8, pp. 198–202, June 2000.Google Scholar
  17. 17.
    L.R. Hochberg, M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D. Penn, and J.P. Donoghue, “Neuronal ensemble control of prosthetic devices by a human with tetraplegia,” Nature, vol. 442, pp. 164–171, 2006.CrossRefGoogle Scholar
  18. 18.
    E.R. Kandel, J.H. Schwartz, and T.M. Jessell, Principles of Neural Science,4th ed. Boston, MA: McGraw-Hill, 2000.Google Scholar
  19. 19.
    R.C. Gesteland, B. Howland, J.Y. Lettvin, and W.H. Pitts, “Comments on microelectrodes,” Proc. IRE, vol. 47, pp. 1856–1862, 1959.CrossRefGoogle Scholar
  20. 20.
    C.D. Ferris, Introduction to Bioinstrumentation.Humana, 1978.Google Scholar
  21. 21.
    R.R. Harrison, “A versatile integrated circuit for the acquisition of biopotentials,” submitted to IEEE Custom Integrated Circuits Conf., pp. 115–122, Sept. 2007.Google Scholar
  22. 22.
    A.C. Metting van Rijn, A. Peper, and C.A. Grimbergen, “High-quality recording of bioelectric events,” Med. Biol. Eng. Comput.,vol. 29, pp. 1035–1044, 1986.Google Scholar
  23. 23.
    V.N. Murthy and E.E. Fetz, “Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys,” Proc. Natl. Acad. Sci. USA, vol. 89, pp. 5670–5674, 1992.CrossRefGoogle Scholar
  24. 24.
    J.P. Donoghue, J.N. Sanes, N.G. Hatsopoulos, and G.Gaal, “Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements,” J. Neurophysiol.,vol. 79, pp. 159–173, 1998.Google Scholar
  25. 25.
    K.V. Shenoy, M.M. Churchland, G. Santhanam, B.M. Yu, and S.I. Ryu, “Influence of movement speed on plan activity in monkey pre-motor cortex and implications for high-performance neural prosthetic system design,” In: Proc. 2003 Intl. Conf. of the IEEE Eng. in Medicine and Biology Soc.,pp. 1897–1900 Cancún, Mexico, 2003.Google Scholar
  26. 26.
    C. Mehring, J. Rickert, E. Vaadia, S. Cardoso de Oliveira, A. Aertsen, S. Rotter, “Inference of hand movements from local field potentials in monkey motor cortex,” Nature Neurosci., vol. 6, pp. 1253–1254, 2003.CrossRefGoogle Scholar
  27. 27.
    H. Scherberger, M.R. Jarvis, and R.A. Andersen, “Cortical local field potential encodes movement intentions in the posterior parietal cortex,” Neuron, vol. 46, pp. 347–354, 2005.Google Scholar
  28. 28.
    B. Pesaran, J.S. Pezaris, M. Sahani, P.P. Mitra, and R.A. Andersen, “Temporal structure in neuronal activity during working memory in macaque parietal cortex,” Nature Neurosci.,vol. 5, pp. 805–811, 2002.CrossRefGoogle Scholar
  29. 29.
    S. Kim, R.A. Normann, R. Harrison, and F. Solzbacher, “Preliminary study of thermal impacts of a microelectrode array implanted in the brain,” In:Proc. 2006 Intl. Conf. of the IEEE Eng. in Medicine and Biology Soc., pp. 2986–2989, New York, NY, 2006.Google Scholar
  30. 30.
    S. Kim, P. Tathireddy, R. Normann, and F. Solzbacher, “In vitro and in vivo study of temperature increases in the brain due to a neural implant,” In: Proc. 3rd Intl. IEEE EMBS Conf. on Neural Engineering, Kohala Coast, HI, 2007.Google Scholar
  31. 31.
    M.S.J. Steyaert, W.M.C. Sansen, and C. Zhongyuan, “A micropower low-noise monolithic instrumentation amplifier for medical purposes,” IEEE J. Solid-State Cir., vol. 22, pp. 1163–1168, Dec. 1987.Google Scholar
  32. 32.
    R.R. Harrison and C. Charles, “A low-power low-noise CMOS amplifier for neural recording applications,” IEEE J. Solid-State Cir.,vol. 38, pp. 958–965, June 2003.Google Scholar
  33. 33.
    E.A. Vittoz and J. Fellrath, “CMOS analog integrated circuits based on weak inversion operation,” IEEE J.Solid-State Cir.,vol.12, pp.224–231, 1977.CrossRefGoogle Scholar
  34. 34.
    C. Mead, Analog VLSI and Neural Systems,Reading, MA: Addison-Wesley, 1989.MATHGoogle Scholar
  35. 35.
    C.C. Enz, F. Krummenacher, and E.A. Vittoz, “An analytical MOS transistor model valid in all regions of operation and dedicated to low-voltage and low-current applications,” Analog Integrat. Circuits Signal Process., vol. 8, pp. 83–114, 1995.CrossRefGoogle Scholar
  36. 36.
    Y. Tsividis, Operation and Modeling of the MOS Transistor, 2nd ed. Boston, MA: McGraw-Hill, 1998.Google Scholar
  37. 37.
    D.A. Johns and K. Martin, Analog Integrated Circuit Design,New York, NY: John Wiley & Sons, 1997.Google Scholar
  38. 38.
    K.S. Guillory and R.A. Normann, “A 100-channel system for real time detection and storage of extracellular spike waveforms,” J. Neurosci. Methods, vol. 91, pp. 21–29, 1999.CrossRefGoogle Scholar
  39. 39.
    K.S. Guillory, A.K. Misener, and A. Pungor, “Hybrid RF/IR transcutaneous telemetry for power and high-bandwidth data,” in Proc. 2004 Intl. Conf. IEEE Engineering in Medicine and Biology Soc. (EMBC 2004), San Francisco, CA, pp. 4338–4340, 2004.Google Scholar
  40. 40.
    K.A. Boahen, “Point-to-point connectivity between neuromorphic chips using address-events,” IEEE Trans. Circuits and Systems II, vol. 47, pp. 416–434, May 2000.Google Scholar
  41. 41.
    R.R. Harrison, “A low-power integrated circuit for adaptive detection of action potentials in noisy signals,” In: Proc. 2003 Intl. Conf. of the IEEE Eng. in Medicine and Biology Soc., pp. 3325–3328, Cancún, Mexico, 2003.Google Scholar
  42. 42.
    P.T. Watkins, G. Santhanam, K.V. Shenoy, and R.R. Harrison, “Validation of adaptive threshold spike detector for neural recording,” In: Proc. 2004 Intl. Conf. of the IEEE Eng. in Medicine and Biology Soc., pp. 4079–4082, San Francisco, CA, 2004.Google Scholar
  43. 43.
    R.R. Harrison, G. Santhanam, and K.V. Shenoy, “Local field potential measurement with low-power analog integrated circuit,” In: Proc. 2004 Intl. Conf. of the IEEE Eng. in Medicine and Biology Soc., pp. 4067–4070, San Francisco, CA, 2004.Google Scholar
  44. 44.
    T.K. Horiuchi, T. Swindell, D. Sander, and P. Abshire, “A low-power CMOS neural amplifier with amplitude measurements for spike sorting,” In: Proc. 2004 IEEE Intl. Symp. on Circuits and Systems, vol. 4, pp. 29–32, Vancouver, BC, Canada, 2004.Google Scholar
  45. 45.
    Z.S. Zumsteg, C. Kemere, S. O’Driscoll, G. Santhanam, R.E. Ahmed, K.V. Shenoy, and T.H. Meng, “Power feasability of implantable digital spike-sorting circuits for neural prosthetic systems,” IEEE Trans. Neural Systems and Rehabilitation, vol. 13, pp. 272–279, Sept. 2005.Google Scholar
  46. 46.
    A.F. Atiya, “Recognition of multiunit neural signals,” IEEE Trans. Biomed. Eng., vol. 39, pp. 723–729, July 1992.Google Scholar
  47. 47.
    G. Santhanam, M.D. Linderman, V. Gilja, A. Afshar, S.I. Ryu, T.H. Meng, and K.V. Shenoy, “HermesB: a continuous neural recording system for freely behaving primates,” IEEE Trans. Biomed. Eng., vol. 54, Issue: 11, pp. 2037–2050, Nov. 2007.Google Scholar
  48. 48.
    D.A. Heldman et.al., ”Local field potential spectral tuning in motor cortex during reaching,” IEEE Trans. Neural Systems and Rehad. Eng., vol. 14, no 2, 2006.Google Scholar
  49. 49.
    A.B. Schwartz, et. al., “Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics,” Neuron, vol. 52, pp.205–220, 2006.CrossRefGoogle Scholar
  50. 50.
    R.R. Harrison, et. al., “A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System” JSSC, Vol. 42, pp. 123–133, 2007.Google Scholar
  51. 51.
    Denison et al. "A 2,2 uW 94 nV/Hz, Chopper-Stabilized Instrumentation Amplifier for EEG Detection in Chronic Implants," JSSC, vol 42, No 12,pp. 2934–2945, 2007.Google Scholar
  52. 52.
    R.F. Yazicioglu, P. Merken, R. Puers, and C. Van Hoof, “A 60 uW 60 nV/rtHz Readout Front-End for Portable Biopotential Acquisition Systems,” IEEE JSSC, vol. 42, no. 5, pp. 1100–1110, 2007.Google Scholar
  53. 53.
    C.D. Salthouse and R. Sarpeshkar, “A practical micropower programmable bandpass filter for use in bionic ears,” JSSC,Vol. 38,pp. 63-7Google Scholar
  54. 54.
    R.R. Harrison, G. Santhanam, and K.V. Shenoy, "Local field potential measurement with low-power analog integrated circuit," In: Proc. 2004 Intl. Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2004), San Francisco, CA, pp. 4067–4070, 2004.Google Scholar
  55. 55.
    R. Sarpeshkar, “Borrowing from biology makes for low-power computing,” IEEE Spectrum, pp. 24–29, May 2006.Google Scholar
  56. 56.
    T.J. Denison et al., “An 8 uW heterodyning chopper amplifier for direct extraction of 2 uVrms brain biomarkers,” ISSCC 2008, paper 8.1.Google Scholar
  57. 57.
    K. Makinwha, Dynamic Offset Compensation Techniques, ISSCC 2007.Google Scholar
  58. 58.
    M. Sanduleanu, “A low noise, low residual offset, chopped amplifier for mixed level applications,” Proc. IEEE Int. Conf. Electron. Cicruits and Systems, 1998, vol. 2, pp. 333–336.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Timothy Denison
    • 1
  • Gregory Molnar
  • Reid R. Harrison
  1. 1.Medtronic Neuromodulation TechnologyUSA

Personalised recommendations