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Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits todoi:10.3389/fncom.2012.00091 pmid:23162459 pmcid:PMC3491427 fatcat:d5kidka35rhujkkwpmbo4oe7da