Compressed Sensing for High Density Neural Recording

  • Jie Zhang
  • Tao Xiong
  • Srinjoy Mitra
  • Ralph Etienne-Cummings
Chapter

Abstract

One of the major challenges in large scale electrophysiology recording devices is the volume of data generated. Typically, each electrode samples the neural signal at 30 KHz with 10 bits digital resolution, a typical speed for neural action potentials acquisition. Hence, a 1000 channel neural probe generates data on the order of 300 Mbits per second. For neuroscientists, this presents an enormous problem in both data transmission and data analysis. Recently, as the demand for high density and distributed neural recording devices grows, tackling the problem of data compression and transmission has become extremely urgent. In this chapter, we first summarize a number of techniques used for neural signal compression. We then focus on the recent development on the use of compressed sensing theory to design more efficient high density neural recording circuits.

References

  1. 1.
    M.A. Wilson, B.L. McNaughton, Dynamics of the hippocampal ensemble code for space. Science 261(5124), 1055–1059 (1993)CrossRefGoogle Scholar
  2. 2.
    S. Mitra, J. Putzeys, F. Battaglia, C.M. Lopez, M. Welkenhuysen, C. Pennartz, C. Van Hoof, R.F. Yazicioglu, 24-channel dual-band wireless neural recorder with activity-dependent power consumption, in 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) (IEEE, New York, 2013), pp. 292–293CrossRefGoogle Scholar
  3. 3.
    R.J. Staba, C.L. Wilson, A. Bragin, I. Fried, J. Engel, Sleep states differentiate single neuron activity recorded from human epileptic hippocampus, entorhinal cortex, and subiculum. J. Neurosci. 22(13), 5694–5704 (2002)Google Scholar
  4. 4.
    J.N. Aziz, K. Abdelhalim, R. Shulyzki, R. Genov, B.L. Bardakjian, M. Derchansky, D. Serletis, P.L. Carlen, 256-channel neural recording and delta compression microsystem with 3d electrodes. IEEE J. Solid State Circuits 44(3), 995–1005 (2009)CrossRefGoogle Scholar
  5. 5.
    D.H. Hubel, T.N. Wiesel, Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1959)CrossRefGoogle Scholar
  6. 6.
    E.M. Maynard, C.T. Nordhausen, R.A. Normann, The Utah intracortical electrode array: a recording structure for potential brain-computer interfaces. Electroencephalogr. Clin. Neurophysiol. 102(3), 228–239 (1997)CrossRefGoogle Scholar
  7. 7.
    C.M. Lopez, A. Andrei, S. Mitra, M. Welkenhuysen, W. Eberle, C. Bartic, R. Puers, R.F. Yazicioglu, G.G. Gielen, An implantable 455-active-electrode 52-channel CMOS neural probe. IEEE J. Solid State Circuits 49(1), 248–261 (2014)CrossRefGoogle Scholar
  8. 8.
    R. Shulyzki, K. Abdelhalim, A. Bagheri, M.T. Salam, C.M. Florez, J.L. Perez Velazquez, P.L. Carlen, R. Genov, 320-channel active probe for high-resolution neuromonitoring and responsive neurostimulation. IEEE Trans. Biomed. Circuits Syst. 9(1), 34–49 (2015)CrossRefGoogle Scholar
  9. 9.
    D. Seo, J.M. Carmena, J.M. Rabaey, M.M. Maharbiz, E. Alon, Model validation of untethered, ultrasonic neural dust motes for cortical recording. J. Neurosci. Methods 244, 114–122 (2015)CrossRefGoogle Scholar
  10. 10.
    A. Khalifa, J. Zhang, M. Leistner, R. Etienne-Cummings, A compact, low-power, fully analog implantable microstimulator, in 2016 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, New York, 2016)Google Scholar
  11. 11.
    F. Chen, A.P. Chandrakasan, V.M. Stojanović, Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors. IEEE J. Solid State Circuits 47(3), 744–756 (2012)CrossRefGoogle Scholar
  12. 12.
    A.P. Chandrakasan, S. Sheng, R.W. Brodersen, Low-power CMOS digital design. IEICE Trans. Electron. 75(4), 371–382 (1992)Google Scholar
  13. 13.
    S. Kim, R. Normann, R. Harrison, F. Solzbacher et al., Preliminary study of the thermal impact of a microelectrode array implanted in the brain, in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS’06 (IEEE, New York, 2006), pp. 2986–2989Google Scholar
  14. 14.
    D.A. Borton, M. Yin, J. Aceros, A. Nurmikko, An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J. Neural Eng. 10(2), 026010 (2013)Google Scholar
  15. 15.
    H. Gao, R.M. Walker, P. Nuyujukian, K.A. Makinwa, K.V. Shenoy, B. Murmann, T.H. Meng, Hermese: a 96-channel full data rate direct neural interface in 0.13μm CMOS. IEEE J. Solid State Circuits 47(4), 1043–1055 (2012)Google Scholar
  16. 16.
    J. Zhang, K. Duncan, Y. Suo, T. Xiong, S. Mitra, T.D. Tran, R. Etienne-Cummings, Communication channel analysis and real time compressed sensing for high density neural recording devices. IEEE Trans. Circuits Syst. Regul. Pap. 63(5), 599–608 (2016)CrossRefGoogle Scholar
  17. 17.
    E.J. Candès, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    D.L. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    S.S. Chen, D.L. Donoho, M.A. Saunders, Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    D. Needell, J.A. Tropp, Cosamp: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26(3), 301–321 (2009)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    J.A. Tropp, A.C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    M.S. Lewicki, B.A. Olshausen, Probabilistic framework for the adaptation and comparison of image codes. JOSA A 16(7), 1587–1601 (1999)CrossRefGoogle Scholar
  23. 23.
    M.S. Lewicki, T.J. Sejnowski, Learning overcomplete representations. Neural Comput. 12(2), 337–365 (2000)CrossRefGoogle Scholar
  24. 24.
    K. Engan, S.O. Aase, J.H. Husøy, Multi-frame compression: theory and design. Signal Process. 80(10), 2121–2140 (2000)CrossRefMATHGoogle Scholar
  25. 25.
    M. Aharon, M. Elad, A. Bruckstein, K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)CrossRefMATHGoogle Scholar
  26. 26.
    M. Mangia, R. Rovatti, G. Setti, Rakeness in the design of analog-to-information conversion of sparse and localized signals. IEEE Trans. Circuits Syst. Regul. Pap. 59(5), 1001–1014 (2012)MathSciNetCrossRefGoogle Scholar
  27. 27.
    J.M. Duarte-Carvajalino, G. Sapiro, Learning to sense sparse signals: simultaneous sensing matrix and sparsifying dictionary optimization. DTIC Document, Technical Report (2008)MATHGoogle Scholar
  28. 28.
    M. Elad, Optimized projections for compressed sensing. IEEE Trans. Signal Process. 55(12), 5695–5702 (2007)MathSciNetCrossRefGoogle Scholar
  29. 29.
    D. Gangopadhyay, E.G. Allstot, A.M. Dixon, K. Natarajan, S. Gupta, D.J. Allstot, Compressed sensing analog front-end for bio-sensor applications. IEEE J. Solid State Circuits 49(2), 426–438 (2014)CrossRefGoogle Scholar
  30. 30.
    Z. Charbiwala, V. Karkare, S. Gibson, D. Marković, M.B. Srivastava, Compressive sensing of neural action potentials using a learned union of supports, in 2011 International Conference on Body Sensor Networks (BSN) (IEEE, New York, 2011), pp. 53–58CrossRefGoogle Scholar
  31. 31.
    M. Shoaran, M.H. Kamal, C. Pollo, P. Vandergheynst, A. Schmid, Compact low-power cortical recording architecture for compressive multichannel data acquisition. IEEE Trans. Biomed. Circuits Syst. 8(6), 857–870 (2014)CrossRefGoogle Scholar
  32. 32.
    J. Zhang, Y. Suo, S. Mitra, S.P. Chin, S. Hsiao, R.F. Yazicioglu, T.D. Tran, R. Etienne-Cummings, An efficient and compact compressed sensing microsystem for implantable neural recordings. IEEE Trans. Biomed. Circuits Syst. 8(4), 485–496 (2014)CrossRefGoogle Scholar
  33. 33.
    M. Zhang, A. Bermak, Compressive acquisition CMOS image sensor: from the algorithm to hardware implementation. IEEE Trans. Very Large Scale Integr. VLSI Syst. 18(3), 490–500 (2010)CrossRefGoogle Scholar
  34. 34.
    D.E. Bellasi, L. Benini, Energy-efficiency analysis of analog and digital compressive sensing in wireless sensors. IEEE Trans. Circuits Syst. Regul. Pap. 62(11), 2718–2729 (2015)MathSciNetCrossRefGoogle Scholar
  35. 35.
    C. Bulach, U. Bihr, M. Ortmanns, Evaluation study of compressed sensing for neural spike recordings, in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, New York, 2012), pp. 3507–3510Google Scholar
  36. 36.
    Q. Zhang, B. Li, Discriminative k-svd for dictionary learning in face recognition, in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2010), pp. 2691–2698Google Scholar
  37. 37.
    R.Q. Quiroga, Z. Nadasdy, Y. Ben-Shaul, Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16(8), 1661–1687 (2004)CrossRefMATHGoogle Scholar
  38. 38.
    T. Sasaki, N. Matsuki, Y. Ikegaya, Action-potential modulation during axonal conduction. Science 331(6017), 599–601 (2011)CrossRefGoogle Scholar
  39. 39.
    P.H. Thakur, H. Lu, S.S. Hsiao, K.O. Johnson, Automated optimal detection and classification of neural action potentials in extra-cellular recordings. J. Neurosci. Methods 162(1), 364–376 (2007)CrossRefGoogle Scholar
  40. 40.
    B. Sun, W. Zhao, X. Zhu, Training-free compressed sensing for wireless neural recording using analysis model and group weighted-minimization. J. Neural Eng. 14(3), 036018 (2017)Google Scholar
  41. 41.
    C.M. Gray, P.E. Maldonado, M. Wilson, B. McNaughton, Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. J. Neurosci. Methods 63(1), 43–54 (1995)CrossRefGoogle Scholar
  42. 42.
    K.D. Harris, D.A. Henze, J. Csicsvari, H. Hirase, G. Buzsáki, Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84(1), 401–414 (2000)CrossRefGoogle Scholar
  43. 43.
    A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm, in Advances in Neural Information Processing Systems, vol. 14 (2001), pp. 849–856Google Scholar
  44. 44.
    W.F. Asaad, E.N. Eskandar, Encoding of both positive and negative reward prediction errors by neurons of the primate lateral prefrontal cortex and caudate nucleus. J. Neurosci. 31(49), 17772–17787 (2011)CrossRefGoogle Scholar
  45. 45.
    D.A. Henze, Z. Borhegyi, J. Csicsvari, A. Mamiya, K.D. Harris, G. Buzsáki, Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84(1), 390–400 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jie Zhang
    • 1
  • Tao Xiong
    • 2
  • Srinjoy Mitra
    • 3
  • Ralph Etienne-Cummings
    • 2
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.Johns Hopkins UniversityBaltimoreUSA
  3. 3.School of EngineeringUniversity of GlasgowGlasgowUK

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