Nonuniformly sampled trivariate empirical mode decomposition

Abstract

Multichannel data-driven time-frequency algorithms, such as the multivariate empirical mode decomposition (MEMD), have emerged as important tools in the analysis of inter-channel dependencies that arise in multivariate data. Such methods employ uniform projection schemes on hyperspheres in order to estimate the local mean, thus requiring dense but underutilised sampling when processing unbalanced data channels. To this end, we propose a nonuniform projection scheme that adapts to the second order statistics of trivariate data; this provides the estimation of the local mean in the case of power imbalances and correlations between the channels. The algorithm is particularly useful for generating a low number of direction vectors within MEMD. Its performance is illustrated on synthetic and real-world data.