Skip to main content
SpringerLink
Log in

Increasing the Temporal Resolution of Dynamic Functional Connectivity with Ensemble Empirical Mode Decomposition

  • Conference paper
  • First Online:
8th European Medical and Biological Engineering Conference (EMBEC 2020)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 80))

Included in the following conference series:

  • 1287 Accesses

Abstract

Understanding the functional organization and execution mechanisms of the brain is one of the key challenges of neuroscience. Functional connectivity emerging from phase synchronization of neural oscillations of different brain regions provides a powerful tool for investigations. While the brain manifests highly dynamic activation patterns, most connectivity work is based on the assumption of signal stationarity. One of the underlying reasons is the problem of obtaining high temporal and spectral resolution at the same time. Dynamic brain connectivity seeks to uncover the dynamism of brain connectivity but the common sliding window methods provide poor temporal resolution, not detailed enough for studying fast cognitive tasks. This paper proposes the use of the Complete Ensemble Empirical Mode Decomposition followed by Hilbert transformation to extract instantaneous frequency and phase information, based on which the phase synchronization between EEG signals can be calculated and detected in every time step of the measurement. The paper demonstrates the suboptimal performance of the sliding window connectivity method and shows that the instantaneous phase based technique is superior to it, capable of tracking changes of connectivity graphs at millisecond steps and detecting the exact time of the activity changes within a ten millisecond margin. These results can open up new opportunities in investigating neurodegenerative diseases, brain plasticity after stroke and understanding the execution of cognitive tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Friston, K.J.: Functional and effective connectivity: a review. Brain Connect. 1, 13–36 (2011). https://doi.org/10.1089/brain.2011.0008

    Article  Google Scholar 

  2. Fornito, A.: An introduction to brain networks. Fundam. Brain Netw. Anal. 1–35 (2016). https://doi.org/10.1016/b978-0-12-407908-3.00001-7

  3. Lang, E.W., Tomé, A.M., Keck, I.R., Górriz-Sáez, J.M., Puntonet, C.G.: Brain connectivity analysis: a short survey. Comput. Intell. Neurosci. 2012, 1–21 (2012). https://doi.org/10.1155/2012/412512

    Article  Google Scholar 

  4. Beckmann, C.F., Deluca, M., Devlin, J.T., Smith, S.M., Hospital, J.R., Ox, O.: Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B: Biol. Sci. 1001–1013 (2005). https://doi.org/10.1098/rstb.2005.1634

  5. Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R., Beckmann, C.F.: Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. U.S.A. 106, 13040–13045 (2009). https://doi.org/10.1073/pnas.0905267106

    Article  Google Scholar 

  6. Glasser, M.F., Smith, S.M., Robinson, E.C., Uğurbil, K., Vidaurre, D., Woolrich, M.W., Van Essen, D.C., Barch, D.M., Miller, K.L., Nichols, T.E., Beckmann, C.F., Salimi-Khorshidi, G., Jenkinson, M.: Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013). https://doi.org/10.1016/j.tics.2013.09.016

    Article  Google Scholar 

  7. Hutchison, R.M., Womelsdorf, T., Allen, E.A., Bandettini, P.A., Calhoun, V.D., Corbetta, M., Della Penna, S., Duyn, J.H., Glover, G.H., Gonzalez-Castillo, J., Handwerker, D.A., Keilholz, S., Kiviniemi, V., Leopold, D.A., de Pasquale, F., Sporns, O., Walter, M., Chang, C.: Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013). https://doi.org/10.1016/j.neuroimage.2013.05.079

    Article  Google Scholar 

  8. Vinck, M., Oostenveld, R., Van Wingerden, M., Battaglia, F., Pennartz, C.M.A.: An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55, 1548–1565 (2011). https://doi.org/10.1016/j.neuroimage.2011.01.055

    Article  Google Scholar 

  9. Allen, E.A., Damaraju, E., Eichele, T., Wu, L., Calhoun, V.D.: EEG signatures of dynamic functional network connectivity states. Brain Topogr. 31(1), 101–116 (2017). https://doi.org/10.1007/s10548-017-0546-2

    Article  Google Scholar 

  10. O’Neill, G.C., Tewarie, P.K., Colclough, G.L., Gascoyne, L.E., Hunt, B.A.E., Morris, P.G., Woolrich, M.W., Brookes, M.J.: Measurement of dynamic task related functional networks using MEG. Neuroimage 146, 667–678 (2017). https://doi.org/10.1016/J.NEUROIMAGE.2016.08.061

    Article  Google Scholar 

  11. Preti, M.G., Bolton, T.A., Van De Ville, D.: The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160, 41–54 (2017). https://doi.org/10.1016/j.neuroimage.2016.12.061

    Article  Google Scholar 

  12. Hindriks, R., Adhikari, M.H., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N.K., Deco, G.: Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? Neuroimage 127, 242–256 (2016). https://doi.org/10.1016/j.neuroimage.2015.11.055

    Article  Google Scholar 

  13. Tary, J.B., Herrera, R.H., Han, J., Van Der Baan, M.: Reviews of geophysics spectral estimation — what is new? What is next? Rev. Geophys. 52, 723–749 (2014). https://doi.org/10.1002/2014RG000461

  14. Kumar, P., Foufoula-Georgiou, E.: Wavelet analysis for geophysical applications. Rev. Geophys. 35, 385–412 (1997). https://doi.org/10.1029/97RG00427

    Article  Google Scholar 

  15. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis (1998). https://www.jstor.org/stable/53161. https://doi.org/10.2307/53161

  16. Huang, N.E.: Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2, 135–156 (2010). https://doi.org/10.1142/S1793536910000422

    Article  MathSciNet  Google Scholar 

  17. Hassan, A.R., Hassan Bhuiyan, M.I.: Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern. Biomed. Eng. 36, 248–255 (2016). https://doi.org/10.1016/j.bbe.2015.11.001

    Article  Google Scholar 

  18. Williams, N.J., Nasuto, S.J., Saddy, J.D.: Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles. J. Neurosci. Methods 250, 22–33 (2015). https://doi.org/10.1016/j.jneumeth.2015.02.007

    Article  Google Scholar 

  19. Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1, 1–41 (2009). https://doi.org/10.1142/S1793536909000047

    Article  Google Scholar 

  20. Torres, M.E., Colominas, M.A., Schlotthauer, G., Flandrin, P.: A complete ensemble empirical mode decomposition with adaptive noise. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, pp. 4144–4147 (2011). https://doi.org/10.1109/ICASSP.2011.5947265

  21. Colominas, M.A., Schlotthauer, G., Torres, M.E.: Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomed. Signal Process. Control 14, 19–29 (2014). https://doi.org/10.1016/J.BSPC.2014.06.009

    Article  Google Scholar 

  22. Carter, G., Knapp, C., Nuttall, A.: Estimation of the magnitude-squared coherence function via overlapped fast Fourier transform processing. IEEE Trans. Audio Electroacoust. 21, 337–344 (1973)

    Article  Google Scholar 

  23. Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., Hallett, M.: Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol. 115, 2292–2307 (2004). https://doi.org/10.1016/J.CLINPH.2004.04.029

    Article  Google Scholar 

  24. Lachaux, J.-P., Rodriguez, E., Martinerie, J., Varela, F.J.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8, 194–208 (1999). https://doi.org/10.1002/(SICI)1097-0193(1999)8:4%3c194:AID-HBM4%3e3.0.CO;2-C

    Article  Google Scholar 

  25. Stam, C.J., Nolte, G., Daffertshofer, A.: Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp. 28, 1178–1193 (2007). https://doi.org/10.1002/hbm.20346

    Article  Google Scholar 

  26. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Snin, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. A Math. Phys. Eng. Sci. 454, 903–995 (1998). https://doi.org/10.1098/rspa.1998.0193

Download references

Acknowledgements

This work has been supported jointly by the Hungarian Government and the European Social Fund (grant number EFOP-3.6.1-16-2016-00015).

Author information

Authors and Affiliations

  1. University of Pannonia, Veszprem, 8200, Hungary

    Mohamed F. Issa, Gyorgy Kozmann & Zoltan Juhasz

  2. Benha University, Benha, 13511, Egypt

    Mohamed F. Issa

Authors
  1. Mohamed F. Issa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gyorgy Kozmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zoltan Juhasz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed F. Issa .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Tomaz Jarm

  2. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Aleksandra Cvetkoska

  3. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Samo Mahnič-Kalamiza

  4. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Damijan Miklavcic

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Issa, M.F., Kozmann, G., Juhasz, Z. (2021). Increasing the Temporal Resolution of Dynamic Functional Connectivity with Ensemble Empirical Mode Decomposition. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64610-3_74

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64609-7

  • Online ISBN: 978-3-030-64610-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation