• Gürkan YılmazEmail author
  • Catherine Dehollain
Part of the Analog Circuits and Signal Processing book series (ACSP)


“How does the brain work?” is one of the Yilmaz questions that has been asked the most throughout the history of medical sciences. The question is addressed from different perspectives by all branches of science, in particular by the life sciences. Although all seek a different answer, there is something common which triggers all these researches: observation. With curiosity, thats what makes the beginning of a scientific study. From electrical engineering perspective, observation regarding this question is performed via recording the electrical signals generated by the neurons and interpreting those results. Recording neural activities plays an important role in numerous applications ranging from brain mapping to implementation of brain-machine interfaces (BMI) to recover lost functions or to understand the mechanisms behind the neurological disorders such as essential tremor, Parkinsons disease [1] and epilepsy [2, 3]. It also constitutes the first step of a closed-loop therapy system which additionally employs a stimulator and a decision mechanism. Such systems are envisaged to record neural anomalies and then stimulate corresponding tissues to cease such activities. Methods for recording the neural signals have evolved to its current state since decades, and the evolution still goes on. This chapter introduces the fundamentals of the new generation neural recording systems: implantable wireless neural recording systems with a case study on in-vivo epilepsy monitoring. Throughout this chapter, firstly, we have defined the problem and then, the motivation to solve this problem, introducing the systems anticipated benefits. Next, challenges to be encountered while realizing such a system have been explained and our approach has been briefly introduced.


Neural recording Epilepsy ECoG iEEG Resective surgery Micro electrode array Ripple Fast ripple Multi-unit activity 


  1. 1.
    G. Deuschl, C. Schade-Brittinger, P. Krack, J. Volkmann, H. Schfer, K. Btzel, C. Daniels, A. Deutschlnder, U. Dillmann, W. Eisner, D. Gruber, W. Hamel, J. Herzog, R. Hilker, S. Klebe, M. Klo, J. Koy, M. Krause, A. Kupsch, D. Lorenz, S. Lorenzl, H.M. Mehdorn, J. R. Moringlane, W. Oertel, M.O. Pinsker, H. Reichmann, A. Reu, G.-H. Schneider, A. Schnitzler, U. Steude, V. Sturm, L. Timmermann, V. Tronnier, T. Trottenberg, L. Wojtecki, E. Wolf, W. Poewe, J. Voges, A randomized trial of deep-brain stimulation for parkinson’s disease. New England J. Med. 355(9), 896–908 (2006)Google Scholar
  2. 2.
    R.R. Harrison, The design of integrated circuits to observe brain activity. Proc. IEEE 96(7), 1203–1216 (2008)CrossRefGoogle Scholar
  3. 3.
    W.H. Theodore, R.S. Fisher, Brain stimulation for epilepsy. Lancet Neurol. 3(2), 111–118 (2004)CrossRefGoogle Scholar
  4. 4.
    J.J. Van Gompel, S.M. Stead, C. Giannini, F.B. Meyer, W.R. Marsh, T. Fountain, E. So, A. Cohen-Gadol, K.H. Lee, G.A. Worrell, Phase i trial: safety and feasibility of intracranial electroencephalography using hybrid subdural electrodes containing macro- and microelectrode arrays. Neurosurg. Focus 25(3), E23 (2008)CrossRefGoogle Scholar
  5. 5.
    J.F. Tllez-Zenteno, R. Dhar, S. Wiebe, Long-term seizure outcomes following epilepsy surgery: a systematic review and meta-analysis. Brain 128(5), 1188–1198 (2005)CrossRefGoogle Scholar
  6. 6.
    E. Carrette, K. Vonck, V. De Herdt, A. Van Dycke, R. El Tahry, A. Meurs, R. Raedt, L. Goossens, M. Van Zandijcke, G. Van Maele, V. Thadani, W. Wadman, D. Van Roost, P. Boon, Predictive factors for outcome of invasive video-eeg monitoring and subsequent resective surgery in patients with refractory epilepsy. Clin. Neurol. Neurosurg. 112(2), 118–126 (2010)CrossRefGoogle Scholar
  7. 7.
    H.M. Hamer, H.H. Morris, E.J. Mascha, M.T. Karafa, W.E. Bingaman, M.D. Bej, R.C. Burgess, D.S. Dinner, N.R. Foldvary, J.F. Hahn, P. Kotagal, I. Najm, E. Wyllie, H.O. Lders, Complications of invasive video-EEG monitoring with subdural grid electrodes. Neurology 58(1), 97–103 (2002)CrossRefGoogle Scholar
  8. 8.
    R.A. Scott, S.D. Lhatoo, J.W. Sander, The treatment of epilepsy in developing countries: where do we go from here? Bull. World Health Organ. 79(4), 344–351 (2001)Google Scholar
  9. 9.
    A. Yakovlev, S. Kim, A. Poon, Implantable biomedical devices: wireless powering and communication. IEEE Commun. Mag. 50(4), 152–159 (2012)CrossRefGoogle Scholar
  10. 10.
    M. Shoaran, C. Pollo, Y. Leblebici, A Schmid, Design techniques and analysis of high-resolution neural recording systems targeting epilepsy focus localization, in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5150–5153 (2012)Google Scholar
  11. 11.
    P.P. Mercier, A.C. Lysaght, S. Bandyopadhyay, A.P. Chandrakasan, K.M. Stankovic, Energy extraction from the biologic battery in the inner ear. Nat. Biotech. 30(12), 1240–1243 (2012)Google Scholar
  12. 12.
    R. Harrison, P. Watkins, R. Kier, R. Lovejoy, D. Black, R. Normann, F. Solzbacher. A low-power integrated circuit for a wireless 100-electrode neural recording system, in Digest of Technical Papers IEEE International Solid-State Circuits Conference (ISSCC 2006), pp. 2258–2267 (2006)Google Scholar
  13. 13.
    C.M. Lopez, D. Prodanov, D. Braeken, I. Gligorijevic, W. Eberle, C. Bartic, R. Puers, G. Gielen, A multichannel integrated circuit for electrical recording of neural activity, with independent channel programmability. IEEE Trans. Biomed. Circuits Syst. 6(2), 101–110 (2012)CrossRefGoogle Scholar
  14. 14.
    C. Hassler, T. Boretius, T. Stieglitz, Polymers for neural implants. J. Polym. Sci. Part B: Polym. Phys. 49(1), 18–33 (2011)CrossRefGoogle Scholar
  15. 15.
    T. Stieglitz, Manufacturing, assembling and packaging of miniaturized neural implants. Microsyst. Technol. 16(5), 723–734 (2010)CrossRefGoogle Scholar
  16. 16.
    C.A. Schevon, A.J. Trevelyan, C.E. Schroeder, R.R. Goodman, G. McKhann, R.G. Emerson, Spatial characterization of interictal high frequency oscillations in epileptic neocortex. Brain 132(11), 3047–3059 (2009)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    D. Yoshor, William H. Bosking, Geoffrey M. Ghose, H.R. J. Maunsell, Receptive fields in human visual cortex mapped with surface electrodes. Cereb. Cortex 17(10), 2293–2302 (2007)Google Scholar
  19. 19.
    T. Akiyama, B. McCoy, C.Y. Go, A. Ochi, I.M. Elliott, M. Akiyama, E.J. Donner, S.K. Weiss, O. Carter Snead, J.T. Rutka, J.M. Drake, H. Otsubo, Focal resection of fast ripples on extraoperative intracranial eeg improves seizure outcome in pediatric epilepsy. Epilepsia 52(10), 1802–1811 (2011)CrossRefGoogle Scholar
  20. 20.
    A. Bragin, I. Mody, C.L. Wilson, J. Engel, Local generation of fast ripples in epileptic brain. J. Neurosci. 22(5), 2012–2021 (2002)Google Scholar
  21. 21.
    S. Wang, I.Z. Wang, J.C. Bulacio, J.C. Mosher, J. Gonzalez-Martinez, A.V. Alexopoulos, I.M. Najm, K.S. Norman, Ripple classification helps to localize the seizure-onset zone in neocortical epilepsy. Epilepsia 54(2), 370–376 (2013)CrossRefGoogle Scholar
  22. 22.
    G. Yilmaz, C. Dehollain, Single frequency wireless power transfer and full-duplex communication system for intracranial epilepsy monitoring. Microelectron. J. 45(12), 1583–1834 (2014)Google Scholar
  23. 23.
    Z. Yang, Q. Zhao, E. Keefer, W. Liu, Noise characterization, modeling, and reduction for in vivo neural recording, in ed. by Y. Bengio, D. Schuurmans, J. Lafferty, C.K.I. Williams, A. Culotta Advances in Neural Information Processing Systems 22, pp. 2160–2168 (2009)Google Scholar
  24. 24.
    D.A. Robinson, The electrical properties of metal microelectrodes. Proc. IEEE 56(6), 1065–1071 (1968)CrossRefGoogle Scholar
  25. 25.
    S.F. Cogan, Neural stimulation and recording electrodes. Ann. Rev. Biomed. Eng. 10(1), 275–309 (2008)CrossRefGoogle Scholar
  26. 26.
    E.W. Keefer, B.R. Botterman, M.I. Romero, A.F. Rossi, G.W. Gross, Carbon nanotube coating improves neuronal recordings. Nat Nano 3(7), 434–439 (2008)CrossRefGoogle Scholar
  27. 27.
    K.A. Ludwig, N.B. Langhals, M.D. Joseph, S.M. Richardson-Burns, J.L. Hendricks, D.R. Kipke, Poly(3,4-ethylenedioxythiophene) (pedot) polymer coatings facilitate smaller neural recording electrodes. J. Neural Eng. 8(1), 014001 (2011)Google Scholar
  28. 28.
    N. Joye, A. Schmid, Y. Leblebici, Electrical modeling of the cellelectrode interface for recording neural activity from high-density microelectrode arrays. Neurocomputing, 73(13):250–259, 2009. Timely Developments in Applied Neural Computing (EANN 2007)/Some Novel Analysis and Learning Methods for Neural Networks (ISNN 2008)/Pattern Recognition in Graphical DomainsGoogle Scholar
  29. 29.
    W. Wattanapanitch, M. Fee, R. Sarpeshkar, An energy-efficient micropower neural recording amplifier. IEEE Trans. Biomed. Circuits Syst. 1(2), 136–147 (2007)CrossRefGoogle Scholar
  30. 30.
    S.C. Moo, L. Wentai, M. Sivaprakasam, Design optimization for integrated neural recording systems. IEEE J. Solid-State Circuits 43(9), 1931–1939 (2008)CrossRefGoogle Scholar
  31. 31.
    M.S.J. Steyaert, W.M.C. Sansen, A micropower low-noise monolithic instrumentation amplifier for medical purposes. IEEE J. Solid-State Circuits 22(6), 1163–1168 (1987)CrossRefGoogle Scholar
  32. 32.
    J. Holleman, B. Otis, A sub-microwatt low-noise amplifier for neural recording, in 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), pp. 3930–3933 (2007)Google Scholar
  33. 33.
    V. Majidzadeh, A. Schmid, Y. Leblebici, Energy efficient low-noise neural recording amplifier with enhanced noise efficiency factor. IEEE Trans. Biomed. Circuits Syst. 5(3), 262–271 (2011)CrossRefGoogle Scholar
  34. 34.
    R.R. Harrison, C. Charles, A low-power low-noise cmos amplifier for neural recording applications. IEEE J. Solid-State Circuits 38(6), 958–965 (2003)CrossRefGoogle Scholar
  35. 35.
    M. Mollazadeh, K. Murari, G. Cauwenberghs, N. Thakor, Micropower cmos integrated low-noise amplification, filtering, and digitization of multimodal neuropotentials. IEEE Trans. Biomed. Circuits Syst. 3(1), 1–10 (2009)CrossRefGoogle Scholar
  36. 36.
    R.R. Harrison, P.T. Watkins, R.J. Kier, R.O. Lovejoy, D.J. Black, B. Greger, F. Solzbacher, A low-power integrated circuit for a wireless 100-electrode neural recording system. IEEE J. Solid-State Circuits 42(1), 123–133 (2007)CrossRefGoogle Scholar
  37. 37.
    J.N.Y. 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
  38. 38.
    M. Rizk, I. Obeid, S.H. Callender, P.D. Wolf, A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system. J. Neural Eng. 4(3), 309 (2007)CrossRefGoogle Scholar
  39. 39.
    Z. Charbiwala, V. Karkare, S. Gibson, D. Markovic, M.B. Srivastava, Compressive sensing of neural action potentials using a learned union of supports, in 2011 International Conference on Body Sensor Networks (BSN), pp. 53–58 (2011)Google Scholar
  40. 40.
    G. Yilmaz, C. Dehollain, Intracranial epilepsy monitoring using wireless neural recording systems, in ed. by S. Carrara, K. Iniewski Handbook of Bioelectronics: Directly Interfacing Electronics and Biological Systems, vol. 008, pp. 389–399. Cambridge University Press, Cambridge (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.EPFL RFIC Research GroupLausanneSwitzerland

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