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Experimental comparison of connectivity measures with simulated EEG signals

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Abstract

Directional connectivity measures exist with different theoretical backgrounds, i.e., information theoretic, parametric-modeling based or phase related. In this paper, we perform the first comparison in this extend of a set of conventional and directed connectivity measures [cross-correlation, coherence, phase slope index (PSI), directed transfer function (DTF), partial-directed coherence (PDC) and transfer entropy (TE)] with eight-node simulation data based on real resting closed eye electroencephalogram (EEG) source signal. The ability of the measures to differentiate the direct causal connections from the non-causal connections was evaluated with the simulated data. Also, the effects of signal-to-noise ratio (SNR) and decimation were explored. All the measures were able to distinguish the direct causal interactions from the non-causal relations. PDC detected less non-causal connections compared to the other measures. Low SNR was tolerated better with DTF and PDC than with the other measures. Decimation affected most the results of TE, DTF and PDC. In conclusion, parametric-modeling-based measures (DTF, PDC) had the highest sensitivity of connections and tolerance to SNR in simulations based on resting closed eye EEG. However, decimation of data has to be carefully considered with these measures.

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References

  1. Astolfi L, Cincotti F, Mattia D, Lai M, Baccalá L, Fallani F, Salinari S, Ursino M, Zavaglia M, Babiloni F (2007) Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp 28:143–157

    Article  PubMed  Google Scholar 

  2. Baccalá LA, Sameshima K (2001) Partial directed coherence: a new concept in neural structure determination. Biol Cybern 84(6):463–474

    Article  PubMed  Google Scholar 

  3. BioSig, “BioSig—toolbox for biosignal analysis” (online). Available: http://biosig.sourceforge.net/

  4. Florin E, Gross J, Pfeifer J, Fink GR, Timmermann L (2010) The effect of filtering on Granger causality based multivariate causality measures. NeuroImage 50(2):577–588

    Article  PubMed  Google Scholar 

  5. Gómez-Herrero G, Wu W, Rutanen K, Soriano M, Pipa G, Vicente R (2010) Assessing coupling dynamics from an ensemble of time series (online). Available: arXiv:1008.0539v1

  6. Kamiński MJ, Blinowska KJ (1991) A new method of the description of the information flow in the brain structures. Biol Cybern 65(3):203–210

    Article  PubMed  Google Scholar 

  7. Kamiński M (2007) Multichannel data analysis in biomedical research. In: Jirsa VK, McIntosh AR (eds) Handbook of brain connectivity. Springer, Berlin, pp 327–355

    Chapter  Google Scholar 

  8. Kuś R, Kamiński M, Blinowska KJ (2004) Determination of EEG activity propagation: pair-wise versus multichannel estimate. IEEE Trans Biomed Eng 51(9):1501–1510

    Article  PubMed  Google Scholar 

  9. Nolte G, Ziehe A, Nikulin VV, Schlögl A, Krämer N, Brismar T, Müller K-R (2008) Robustly estimating the flow direction of information in complex physical systems. Phys Rev Lett 100(23):234101

    Article  PubMed  Google Scholar 

  10. Nolte G, Ziehe A, Nikulin VV, Schlögl A, Krämer N, Brismar T, Müller K-R, Phase-slope index—software (online). Available: http://ml.cs.tu-berlin.de/causality/

  11. Pereda E, Quiroga RQ, Bhattacharya J (2005) Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol 77(1–2):1–37

    Article  PubMed  Google Scholar 

  12. Porcaro C, Zappasodi F, Rossini PM, Tecchio F (2009) Choice of multivariate autoregressive model order affecting real network functional connectivity estimate. Clin Neurophysiol 120:436–448

    Article  PubMed  Google Scholar 

  13. Rutanen K, Gómez-Herrero G, TIM-toolbox for estimation of information-theoretic measures from time-series (online). Available: http://www.cs.tut.fi/~timhome/tim.htm

  14. Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85:461–464

    Article  PubMed  CAS  Google Scholar 

  15. Vélez-Pérez H, Louis-Dorr V, Ranta R, Dufaut M (2008) Connectivity estimation of three parametric methods on simulated electroencephalogram signals. Conf Proc IEEE Eng Med Biol Soc 2008:2606–2609

    PubMed  Google Scholar 

  16. Winterhalder M, Schelter B, Hesse W, Schwab K, Leistritz L, Timmer J, Witte H (2006) Detection of directed information flow in biosignals. Biomed Tech (Berl) 51(5–6):281–287

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Academy of Finland.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Oulu, Box 4500, 90014, Oulu, Finland

    Minna J. Silfverhuth, Jukka Kortelainen & Tapio Seppänen

  2. Centre for Environmental and Respiratory Health Research, Institute of Biomedicine, University of Oulu, BOX 5000, 90014, Oulu, Finland

    Heidi Hintsala

Authors
  1. Minna J. Silfverhuth
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  2. Heidi Hintsala
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Correspondence to Minna J. Silfverhuth.

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Silfverhuth, M.J., Hintsala, H., Kortelainen, J. et al. Experimental comparison of connectivity measures with simulated EEG signals. Med Biol Eng Comput 50, 683–688 (2012). https://doi.org/10.1007/s11517-012-0911-y

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