Stationarity stopping criterion for matching pursuit—framework and encephalographic illustration

We present a new stopping criterion for the matching pursuit (MP) algorithm, based on evaluating stationarity of the residua of the consecutive MP iterations. The new stopping criterion is based on a model of a nonstationary signal, which assumes that the part of the signal that is of interest is nonstationary and contaminated by a weakly stationary noise. Mean- and variance-stationarity of the residua obtained from each step of MP is evaluated by means of dedicated statistical tests—the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test and the White test, respectively. We illustrate the proposed concept by an example in which we analyse magnetoencephalographic (MEG) data.