An Active Electrode Readout Circuit

  • Jiawei Xu
  • Refet Firat Yazicioglu
  • Chris Van Hoof
  • Kofi Makinwa
Part of the Analog Circuits and Signal Processing book series (ACSP)


This chapter presents an AE with gain, which results in a better noise-power tradeoff than a buffer-based AE. The proposed AE utilizes an instrumentation amplifier (IA) with state-of-the-art performance, making it suitable for dry-electrode EEG acquisition. The IA is based on an AC-coupled chopper amplifier, equipped with impedance boosting and digitally-assisted offset trimming. These techniques improve the IA’s input impedance and reduce its intrinsic offset, respectively. Mismatch between two AEs is usually a dominant contributor of a low CMRR. As shown in this chapter, the mismatch problem is addressed by a back-end common-mode feedback (CMFB) circuit, leading to a 30 dB improvement of CMRR. In addition, the AE-based EEG readout circuit is benchmarked with a reference EEG system to demonstrate the AE’s benefits, namely, reduced sensitivity to cable motion artifacts and mains interference.


CMFB Impedance boosting DC servo Chopper amplifier 


  1. 1.
    N. Verma, A. Shoeb, A.J. Bohorquez, et al., A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE J. Solid State Circuits 45(4), 804–816 (2010)CrossRefGoogle Scholar
  2. 2.
    Q. Fan, F. Sebastiano, H. Huijsing, K.A.A. Makinwa, A 1.8μW 60nV/√Hz capacitively-coupled chopper instrumentation amplifier in 65nm CMOS for wireless sensor nodes. IEEE J. Solid-State Circuits 46(7), 1534–1543 (2011)CrossRefGoogle Scholar
  3. 3.
    R. Wu, K.A.A. Makinwa, J.H. Huijsing, A chopper current-feedback instrumentation amplifier with a 1mHz 1/f noise corner and an AC-coupled ripple reduction loop. IEEE J. Solid State Circuits 44(12), 3232–3243 (2009)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    M. Sanduleanu et al., A low noise, low residual offset, chopped amplifier for mixed level applications. Proc. IEEE Int. Conf. Electron Circuits Syst. 2, 333–336 (1998)Google Scholar
  6. 6.
    C.C. Enz, G.C. Temes, Circuit techniques for reducing the effects of op-amp imperfections: Autozeroing, correlated double sampling, and chopper stabilization. Proc. IEEE 84, 1584–1614 (1996)CrossRefGoogle Scholar
  7. 7.
    R.F. Yazicioglu, P. Merken, R. Puers, et al., A 60μW 60nV/√Hz readout front-end for portable biopotential acquisition systems. IEEE J. Solid State Circuits 42(5), 1100–1110 (2007)CrossRefGoogle Scholar
  8. 8.
    B.B. Winter, J.G. Webster, Driven-Right-Leg circuit design. IEEE Trans. Biomed. Eng. 30(1), 62–66 (1983)CrossRefGoogle Scholar
  9. 9.
    T. Degen, H. Jackel, Enhancing interference rejection of preamplified electrodes by automated gain adaption. IEEE Trans. Biomed. Eng. 51(11), 2031–2039 (2004)CrossRefGoogle Scholar
  10. 10.
    T. Jochum, T. Denison, P. Wolf, Integrated circuit amplifiers for multi-electrode intracortical recording. J. Neural Eng. 6(1), 012001 (2009)CrossRefGoogle Scholar
  11. 11.
    T. Denison, K. Consoer, A. Kelly et al., A 2.2μW 94nV/√Hz, chopper-stabilized instrumentation amplifier for EEG detection in chronic implants, Digest of ISSCC, (Feb. 2007), pp. 162–594Google Scholar
  12. 12.
    X. Zou, W. Liew, L. Yao, L. Yong, A 1V 450nW fully integrated programmable biomedical sensor interface chip, IEEE J. Solid-State Circuits, 44(4) (Apr. 2009), pp. 1067–1077Google Scholar
  13. 13.
  14. 14.
    Y.M. Chi, T.-P. Jung, G. Cauwenberghs, Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Rev. Biomed. Eng. 3, 106–119 (2010)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    L. Brown, J. van de Molengraft, R. F. Yazicioglu, T. Torfs, J. Penders, C. Van Hoof, A low-power, wireless, 8-channel EEG monitoring headset, IEEE EMBC, (Aug. 2010), pp. 4197–4200Google Scholar
  17. 17.
    R. Matthews, P. J. Turner, N. J. McDonald, K. Ermolaev, T. Mc Manus, R. A. Shelby, M. Steindorf, Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes, IEEE EMBC, (Aug. 2008), pp. 5871–5875Google Scholar
  18. 18.
    J.R. Estepp, J.C. Christensen, J.W. Monnin, I.M. Davis, G.F. Wilson, Validation of a dry electrode system for EEG. Human Factors and Ergonomics Society Annual Meeting 53(18), 1171–1175 (2009)CrossRefGoogle Scholar
  19. 19.
    G. Gargiulo, P. Bifulco, R. A. Calvo, M. Cesarelli, C. Jin, A. van Schaik, A mobile EEG system with dry electrodes, IEEE Biomedical Circuits and Systems Conference, (Nov. 2008), pp. 273–276Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jiawei Xu
    • 1
  • Refet Firat Yazicioglu
    • 2
  • Chris Van Hoof
    • 3
  • Kofi Makinwa
    • 4
  1. 1.Holst Centre / imecEindhovenThe Netherlands
  2. 2.Galvani BioelectronicsStevenageUK
  3. 3.ESAT-MICASKU Leuven / imecLeuvenBelgium
  4. 4.Delft University of TechnologyDelftThe Netherlands

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