Skip to main content
Log in

A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Andrade A, Kyberd PJ, Taffler SD (2003) A novel spectral representation of electromyographic signals. In: Leder RS ed. Proceedings of IEEE Engineering in Medicine and Biology Society—25th Annual International Conference. Cancun, Mexico, pp. 2598–2601.

  • Balocchi R, Menicucci D, Santarcangelo E, Sebastiani L, Gemignani A, Ghelarducci B, Varanini M (2004) Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition. Chaos Solitons Fractals 20: 171–177.

    Article  Google Scholar 

  • Bear M, Connors B, Paradiso M (2001) Neuroscience: Exploring the Brain, 2nd edn. Lippincott, Williams, and Wilkins.

    Google Scholar 

  • Bendat J, Piersol A (1986) The Hilbert transform. In: Random Data: Analysis and Measurement Procedures, 2nd edn. John Wiley and Sons, Inc., pp. 484–516.

  • Bernat E, Williams W, Gehring W (2005) Decomposing ERP time-frequency energy using PCA. Clin. Neurophysiol. 116: 1314–1334.

    Article  PubMed  Google Scholar 

  • Breakspear M, Williams LM, Stam CJ (2004) A novel method for the topographic analysis of neural activity reveals formation and dissolution of “dynamic cell assemblies”. J. Comput. Neurosci. 16(1): 49–68.

    Article  PubMed  Google Scholar 

  • Bressler SL, Coppola R, Nakamura R (1993) Episodic multiregional cortical coherence at multiple frequencies during visual task performance. Nature 366(6451): 153–156.

    Article  PubMed  CAS  Google Scholar 

  • Bruns A (2004). Fourier-, Hilbert-, and wavelet-based signal analysis: Are they really different approaches? J. Neurosci. Methods 137: 321–332.

    Article  PubMed  Google Scholar 

  • Bullmor E, Fadili J, Maxim V, Sendur L, Whitcher B, Suckling J, Brammer M, Breakspear M (2004) Wavelets and functional magnetic resonance imaging of the human brain. Neuroimage 23(Suppl 1): S234–249.

    Article  Google Scholar 

  • Carter GC (1987) Coherence and time delay estimation. Proc. IEEE 75(2): 236–255.

    Google Scholar 

  • Chatfield C (1996). The Analysis of Time Series. An Introduction, 5th edn. Texts in Statistical Science. Chapman and Hall/CRC.

  • Daubechies I (1992). Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics (SIAM). Philadelphia, PA, USA.

  • David O, Cosmelli D, Friston K (2004) Evaluation of different measures of functional connectivity using a neural mass model. Neuroimage 21: 659–673.

    Article  PubMed  Google Scholar 

  • David O, Cosmelli D, Lachaux J-P, Baillet S, Garnero L, Martinerie J (2002) A theoretical and experimental introduction to the non-invasive study of large-scale neural phase synchronization in human beings (invited paper). Int. J. Comput. Cogn. 1(4): 53–77.

    Google Scholar 

  • Deering R, Kaiser J (2005) The use of a masking signal to improve empirical mode decomposition. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 485–489.

  • Destexhe A, Contreras D, Steriade M (1999) Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. J. Neurosci. Methods 19: 4595–4608.

    CAS  Google Scholar 

  • Dringenberg H, Diavolitsis P (2002) Electroencephalographic activation by fluoxetine in rats: Role of 5-HT receptors and enhancement of concurrent acetylcholinesterase inhibitor treatment. Neuropharmacology 42: 154–161.

    Article  PubMed  CAS  Google Scholar 

  • Farge M (1992) Wavelet transforms and their applications to turbulence. Annu. Rev. Fluid Mech. 24: 395–457.

    Article  Google Scholar 

  • Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11: 112–114.

    Article  Google Scholar 

  • Friston K, Stephan K, Frackowiak R (1997) Transient phase-locking and dynamic correlations: Are they the same thing? Hum Brain Mapp. 5: 48–57.

    Article  Google Scholar 

  • Gabor D (1946) Theory of communication. J. Inst. Electr. Eng. 93: 429–457.

    Google Scholar 

  • Gray C (1999) The temporal correlation hypothesis of visual feature integration: Still alive and well. Neuron 24: 31–47.

    Article  PubMed  CAS  Google Scholar 

  • Gray C, Koenig P, Engel A, Singer W (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338: 334–337.

    Article  PubMed  CAS  Google Scholar 

  • Grinsted A, Moore J, Jevrejeva S (2004a) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11: 561–566.

    Google Scholar 

  • Grinsted A, Moore J, Jevrejeva S (2004b) Cross wavelet and wavelet coherence Matlab toolbox. http://www.pol.ac.uk/home/ research/waveletcoherence/.

  • Huang N, Chern C, Huang K, Salvino L, Long S, Fan K (2001) A new spectral representation of earthquake data: Hilbert spectral analysis of Station TCU129, Chi-Chi, Taiwan, 21 September, 1999. Bull. Seismol. Soc. Am. 91(5): 1310–1338.

    Article  Google Scholar 

  • Huang N, Shen S (2005) Hilbert-Huang transform and its applications. Interdisciplinary Mathematical Sciences - Volume 5. World Scientific Publishing Co. Pte. Ltd.

  • Huang N, Shen Z, Long S, Wu M, Shih H, Zheng Q, Yen N-C, Tung C, Liu H (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. 454: 903–995.

    Article  Google Scholar 

  • Huang N, Wu M-L, Qu W, Long S, Shen S, Zhang J (2003) Applications of Hilbert-Huang transform to non-stationary financial time series analysis. Appl. Stoch. Models Bus. Ind. 19: 245–268.

    Article  Google Scholar 

  • Jiruska P, Proks J, Drbal O, Sovka P, Marusic P, Mares P (2005) Comparison of different methods of time shift measurement in EEG. Physiol. Res. 54: 459–465.

    PubMed  CAS  Google Scholar 

  • Kandel E, Schwartz J, Jessell T (1991) Principles of Neural Science, 3rd edn. Appleton and Lange, Norwalk, CT.

    Google Scholar 

  • Kaplan D (1996) Surrogate Data. Nonlinear Time Series Software. Macalaster College, Minnesota website at http://www.macalester.edu/~kaplan/software/.

  • Kiebel SJ, Tallon-Baudry C, Friston K (2005) Parametric analysis of oscillatory activity as measured with EEG/MEG. Hum. Brain Mapp. 26: 170–177.

    Article  PubMed  Google Scholar 

  • Klimesch W (1995) Memory processing described as brain oscillations in the EEG-alpha and theta bands. Psycoloquy 6(6).

  • Koenig P, Engel A, Roelfsema P, Singer W (1995) How precise is neuronal synchronization? Neural Comput. 7: 469–485.

    Google Scholar 

  • Lachaux JP, Lutz A, Rudrauf D, Cosmelli D, Le Van Quyen M, Martinerie J, Varela F (2002) Estimating the time-course of coherence between single-trial brain signals: An introduction to wavelet coherence. Neurophysiol. Clin. 32(1–3): 157–174.

    Article  PubMed  Google Scholar 

  • Lachaux J-P, Rodriguez E, Le Van Quyen M, Martinerie J, Varela F (2000) Studying single trials of phase synchronous activity in the brain. Int. J. Bifurc. Chaos 10: 2429–2439.

    Google Scholar 

  • Lachaux J-P, Rodriguez E, Martinerie J, Varela F (1999) Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8: 194–208.

    Article  PubMed  CAS  Google Scholar 

  • Le Van Quyen M, Foucher J, Lachaux J-P, Rodriguez E, Lutz A, Martinerie J, Varela F (2001) Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony. J. Neurosci. Methods 111: 83–98.

  • Liang H, Bressler S, Buffalo E, Desimone R, Fries P (2005) Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention. Biol. Cybern. 92: 380–392.

    Article  PubMed  Google Scholar 

  • Liang H, Lin Z, McCallum R (2000) Artifact reduction in electrogastrogram based on the empirical mode decomposition method. Med. Biol. Eng. Comput. 38: 35–41.

    Article  PubMed  CAS  Google Scholar 

  • Magrin-Chagnolleau I, Baraniuk R (1999) Empirical mode decomposition based time-frequency attributes. In: Proceedings of the 69th SEG Meeting. Houston, Texas, USA, vol. 18, pp. 1949–1952.

  • McKeown M, Saab R, Abu-Gharbieh R (2005) A combined independent component analysis (ICA)/empirical mode decomposition (EMD) method to infer cortico-muscular coupling. In: Conference on Neural Engineering. Proceedings of the 2nd International IEEE EMBS. Arlington, Virginia, pp. 679–682.

  • Menon V, Freeman W, Cutillo B, Desmond J, Ward M, Bressler SL, Laxer K, Barbaro N, Gevins A (1996) Spatio-temporal correlations in human gamma band electrocortigrams. Electroenceph. Clin. Neurophysiol. 98: 89–102.

    Article  PubMed  CAS  Google Scholar 

  • Miltner W, Braun C, Arnold M, Witte H, Taub E (1999) Coherence of gamma-band EEG activity as a basis for associative learning. Nature 397: 434–436.

    Article  PubMed  CAS  Google Scholar 

  • Pare D, Curro”Dossi R, Steriade M (1990) Neuronal basis of the parkinsonian resting tremor: a hypothesis and its implications for treatment. Neuroscience 35: 217–226.

    Article  PubMed  CAS  Google Scholar 

  • Pfurtscheller G, Schoegl A (2003) The BCI competition 2003. Dept. of Med. Informatics, Graz University of Technology. Website via http://ida.first.fraunhofer.de/homepages/ida/.

  • Pradhan N, Dutt D, Satyam S (1993) A mimetic-based frequency domain technique for automatic generation of EEG reports. Comput. Biol. Med. 23(1): 15–20.

    Article  PubMed  CAS  Google Scholar 

  • Quian Quiroga R, Kraskov A, Kreuz T, Grassberger P (2002) Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Phys. Rev. E 65: 041903.

    Article  CAS  Google Scholar 

  • Rilling G, Flandrin P, Goncalves P (2003) On empirical mode decomposition and its algorithms. In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03.

  • Rodriguez E, George N, Lachaux J-P, Martinerie J, Ranault B, Varela F (1999) Perception”s shadow: long-distance synchronization of human brain activity. Nature 397: 430–433.

    Article  PubMed  CAS  Google Scholar 

  • Roelfsema P, Engel A, Koenig P, Singer W (1997) Visuomotor integration is associated with zero time-lag synchronization among cortical areas. Nature 385: 157–161.

    Article  PubMed  CAS  Google Scholar 

  • Rosenblum M, Pikovsky A, Kurths J, Schaefer C, Tass P (2001) Phase synchronization: from theory to data analysis (Chapter 9). Vol. 4, Neuroinformatics of Handbook of Biological Physics. Elsevier Science.

  • Rosenblum M, Pikovsky A, Kurths J (2001) Phase synchronization and coupled oscillators. IEEE Trans. Circuits Syst.-I 44(10): 874–881.

    Article  Google Scholar 

  • Saab R, McKeown M, Myers L, Abu-Gharbich R (2005) A wavelet based approach for the detection of coupling in EEG signals. In: 2nd International IEEE EMBS Conference on Neural Engineering. Arlington, Virginia, pp. 616–320.

  • Schiff SJ, So P, Chang T, Burke RE, Sauer T (1996) Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. Phys. Rev. E 54(6): 6708–6724.

    Article  CAS  Google Scholar 

  • Schreiber T, Schmitz A (1996) Improved surrogate data for nonlinearity tests. Phys. Rev. Lett. 77(4): 635–638.

    Article  CAS  Google Scholar 

  • Schwartz M, Bennett W, Stein S (1966) Communication Systems and Techniques. McGraw-Hill, Inc.

  • Shiavi R (1991) Introduction to Applied Statistical Signal Analysis. Richard D. Irwin, Inc., and Aksen Associates, Inc.

  • Singer W, Gray C (1995) Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18: 555–586.

    Article  PubMed  CAS  Google Scholar 

  • Srinivasan R, Russell D, Edelman G, Tononi G (1999) Increased synchronization of neuromagnetic responses during conscious perception. J. Neurosci. 19: 5435–5448.

    PubMed  CAS  Google Scholar 

  • Sweeney-Reed CM, Andrade A, Nasuto S (2004) Empirical mode decomposition of EEG signals for synchronisation analysis. In: IEEE EMBSS UKRI Postgraduate Conference on Biomedical Engineering and Medical Physics. Southampton, pp. 15–16.

  • Sweeney-Reed CM, Howroyd JD, Andrade ADO, Nasuto SJ (2005) Empirical mode decomposition for isolation of neural assemblies underlying cognitive acts. In: IEEE EMBSS UKRI Postgraduate Conference on Biomedical Engineering and Medical Physics. University of Reading, UK, pp. 21–22.

  • Tass P, Rosenblum M, Weule J, Kurths J, Pikovsky A, Volkmann J, Schnitzler A, Freund H-J (1998) Detection of n:m phase locking from noisy data: application of magnetoencephalography. Phys. Rev. Lett. 81(15): 3291–3294.

    Article  CAS  Google Scholar 

  • Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer J (1992) Testing for nonlineaity in time series: the method of surrogate data. Physica D 85(77).

  • Torrence C, Compo G (1998) A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79(1): 61–78.

    Article  Google Scholar 

  • Torrence C, Webster P (1999) Interdecadal changes in the ENSO-monsoon system. J. Climatol. 12: 2679–2690.

    Article  Google Scholar 

  • Varela F (1995) Resonant cell assemblies. A new approach to cognitive function and neural synchrony. Biol. Res. 28: 81–95.

    CAS  Google Scholar 

  • Varela F, Lachaux J-P, Rodriguez E, Martinerie J (2001) The brainweb: phase synchronisation and large-scale integration. Nature Rev. Neurosci. 2: 228–239.

    Article  Google Scholar 

  • Whitton J, Lue F, Moldofsky H (1978) A spectral method for removing eye-movement artefacts from the EEG. Electroenceph. Clin. Neurophysiol. 44: 735–741.

    Article  PubMed  CAS  Google Scholar 

  • Wu Z, Huang N (2004) A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A 460: 1597–1611.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. M. Sweeney-Reed.

Additional information

Action Editor: Carson C. Chow

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sweeney-Reed, C.M., Nasuto, S.J. A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition. J Comput Neurosci 23, 79–111 (2007). https://doi.org/10.1007/s10827-007-0020-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-007-0020-3

Keywords

Navigation