EEG Monitoring: Performance Comparison of Compressive Sensing Reconstruction Algorithms

  • Meenu RaniEmail author
  • S. B. Dhok
  • R. B. Deshmukh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)


EEG represents the electrical activity across brain. This activity is monitored to diagnose the diseases due to brain disorders, like epilepsy, coma, sleep disorders, etc. To record EEG signal, a minimum of 21 electrodes are placed across the scalp, which generates huge amount of data. To handle this data, compressive sensing (CS) proves itself to be a better candidate than the traditional sampling. CS generates far fewer samples than that suggested by Nyquist rate and still allows faithful reconstruction. This paper compares the performance of CS reconstruction algorithms in reconstructing the EEG signal back from compressive measurements. The algorithms compared from convex optimization are basis pursuit (BP) and basis pursuit denoising (BPDN) and from greedy algorithms are orthogonal matching pursuit (OMP) and compressive sampling matching pursuit (CoSaMP). The performance of these algorithms is compared on the basis of speed and reconstruction efficiency.


Compressive sensing EEG-monitoring Random demodulator Basis pursuit OMP CoSaMP 


  1. 1.
    A. Lay-Ekuakille et al., Entropy index in quantitative EEG measurement for diagnosis accuracy. IEEE Trans. Inst. Meas. 63, 1440–1450 (2014)CrossRefGoogle Scholar
  2. 2.
    A. Lay-Ekuakille, et al., Multidimensional analysis of EEG features using advanced spectral estimates for diagnosis accuracy. in IEEE International Symposium on Medical Measurements and Applications (MeMeA), (Gatineau, QC, 2013) pp. 237–240.
  3. 3.
    Vergallo, P., et al.: Identification of Visual Evoked Potentials in EEG detection by emprical mode decomposition, in IEEE 11th International Multi-Conference on Systems Signals and Devices (SSD14) (Barcelona, 2014) pp. 1–5.
  4. 4.
    P. Vergallo, et al., Spatial filtering to detect brain sources from EEG measurements, in IEEE national Symposium on Medical Measurements and Applications (MeMeA) (Lisboa, 2014), pp. 1–5Google Scholar
  5. 5.
    S. Aviyente, Compressed Sensing Framework for EEG Compression, in IEEE/SP 14th Workshop on statistical signal processing (Madison, WI, USA, 2007) pp. 181–184Google Scholar
  6. 6.
    Z. Zhang et al., Compressed Sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware. IEEE Trans. on Biomed. Engg. 60(1), 221–224 (2013). Scholar
  7. 7.
    A.M. Abdulghani et al., Compressive sensing scalp EEG signals: implementations and practical performance. E. Med. Biol. Eng. Comp. 50, 1137–1145 (2012)CrossRefGoogle Scholar
  8. 8.
    E.J. Candès et al., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. on Inf. Theory 52(2), 489–509 (2006). Scholar
  9. 9.
    D.L. Donoho, Compressed sensing. IEEE Trans. on Inf. Theory 52(4), 1289–1306 (2006). Scholar
  10. 10.
    R.G. Baraniuk, Compressive sensing [lecture notes]. IEEE Sig. Process. Mag. 24(4), 118–121 (2007). Scholar
  11. 11.
    E.J. Candès, M.B. Wakin, An Introduction to compressive sampling. IEEE Sig. Process. Mag. 25(2), 21–30 (2008). Scholar
  12. 12.
    R. Baraniuk et al., An introduction to compressive sensing. OpenStax-CNX. April 2, 2011.
  13. 13.
    M.F. Duarte et al., Single-pixel imaging via compressive sampling. IEEE Sig. Process. Mag. 25(2), 83–91 (2008). Scholar
  14. 14.
    J.A. Tropp et al., Beyond nyquist: efficient sampling of sparse bandlimited signals. IEEE Trans. Inf. Theory 56(1), 520–544 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mishali, M., Eldar, Y.C.: From theory to practice: sub-nyquist sampling of sparse wideband analog signals. IEEE J. Sel. Top. Sig. Process. 4(2), 375–391 (2010). Scholar
  16. 16.
    J.P. Slavinsky, et al., The compressive multiplexer for multi-channel compressive sensing, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, pp. 3980–3983 (2011).
  17. 17.
    M. Rani et al., A systematic review of compressive sensing: concepts. Implementations Appl. IEEE Access 6, 4875–4894 (2018)CrossRefGoogle Scholar
  18. 18.
    D.L. Donoho, For most large underdetermined systems of linear equations the minimal L1-norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59(6), 797–829 (2006). Scholar
  19. 19.
    E.J. Candès, T. Tao, Decoding by linear programming. IEEE. Trans. Inform. Theory 51(12), 4203–4215 (2005). Scholar
  20. 20.
    S. Chen et al., Atomic decomposition by basis pursuit. SIAM J. Sci Comp. 20(1), 33–61 (1999). Scholar
  21. 21.
    G.H. Golub, C.F. Van Loan, Matrix Analysis. Matrix Computations, 4th ed. (The Johns Hopkins University Press, Baltimore, MD, 2013) ch. 2, pp. 68–73Google Scholar
  22. 22.
    G. Strang, Linear Algebra and Its Applications, 4th edn. (Thomson on Learning Inc, USA, 2006)zbMATHGoogle Scholar
  23. 23.
    E.J. Candès, J. Romberg, Sparsity and incoherence in compressive sampling. Inv. Prob. 23(3), 969–985 (2007). Scholar
  24. 24.
    Goldberger, A.L, et al.: PhysioBank, physioToolkit, and physioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 [Circulation Electronic Pages;]; 2000 (June 13)
  25. 25.
    Pati, Y.C., et al., Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, in Proceedings 27th Asilomar Conference Signals, Systems, and Computers (Pacific Grove, CA, 1993), vol. 1, pp. 40–44Google Scholar
  26. 26.
    D. Needell, J. Tropp, CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal., 26(3), 301–321 (2009). Scholar
  27. 27.
    M. Grant, S. Boyd, CVX: Matlab software for disciplined convex programming, version 2.0 beta (September 2013).
  28. 28.
    Becker, S., A matlab function: CoSaMP and OMP for sparse recovery, version 1.7, Aug 2016. Available online at:

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Visvesvaraya National Institute of TechnologyNagpurIndia

Personalised recommendations