Synchronization Between Sources: Emerging Methods for Understanding Large-Scale Functional Networks in the Human Brain

Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 2)


This chapter summarizes currently available techniques for measuring synchronization between neural sources identified through EEG and MEG recordings. First the evidence for the involvement of neural synchronization in the implementation of cognitive processes is described. This involvement is mainly through the provision of high-quality communication between active brain regions, allowing integration of processing activities through the exchange of information and control signals. Second, we describe several useful techniques for obtaining phase information from time series of EEG and MEG records, and measuring phase locking or phase coherence using these methods. These include wavelet analysis and the analytic signal using the Hilbert transform for obtaining phase information, and phase-locking value and coherence for obtaining useful indices of synchronization. Finally, we summarize several available techniques for locating neural sources of EEG and MEG records and describe the use of the phase-locking measurements in ascertaining synchronization between sources located with these techniques. The techniques include those involving blind separation of sources, such as independent component analysis or principle component analysis, and those involving use of brain anatomy to constrain source locations, such as beamformer or LORETA. We also provide a few examples of published or forthcoming research that has used these approaches. All of the techniques described are available either in commercial software (such as BESA and MATLAB) or in freeware that runs in MATLAB (such as EEGLAB, Fieldtrip, Brainstorm). Some custom programming might be required (e.g., in MATLAB using the Signal Processing Toolbox) to implement some of the measurements.


Independent Component Analysis Wavelet Coefficient Essential Tremor Independent Component Analysis Phase Synchronization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Preparation of this chapter was supported by a grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada.


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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of British ColumbiaVancouverCanada
  2. 2.Department of Psychology; The Brain Research CentreUniversity of British ColumbiaVancouverCanada

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