  We consider the problem of change-point estimation of the instantaneous phase
of an observed time series. Such change points, or phase shifts, can be markers
of information transfer in complex systems; their analysis occurring in
geology, biology and physics, but most notably in neuroscience. We develop two
non-parametric approaches to this problem: the cumulative summation (CUSUM) and
phase derivative (PD) estimators. In general the CUSUM estimator has higher
power for identifying single shift events, while the PD estimator has better
temporal resolution for multiple ones. A system of weakly coupled Rossler
attractors provides an application in which there are high levels of systematic
and time-dependent noise. Shift identification is also performed on beta-band
activity from electroencephalogram recordings of a visual attention task, an
unsupervised application which requires high temporal resolution.
