Partial noise assisted multivariate EMD: An improved noise assisted method for multivariate signals decomposition

Empirical mode decomposition (EMD) is widely used in the decomposition of non-stationary signals, contributing to accurate time-frequency analysis in different scales. With the increasing demand for dealing with multivariate signals, it's of practical significance to extend univariate EMD to multivariate EMD (MEMD). Following the MEMD, a noised assisted MEMD (NA-MEMD) method has been proposed recently to restrain the mode mixing phenomenon. However the standard NA-MEMD method is prone to obtaining unstable performance, and suffering high computational complexity. In this paper, a partial noise assisted multivariate EMD (PNA-MEMD) method is proposed to resolve the problems. In PNA-MEMD, high-frequency band-limited noise instead of the original white noise is utilized to assist the decomposition process. The numerical simulations and application to motor imagery EEG data demonstrate the superiority of proposed method, in contrast with the standard MEMD and NA-MEMD.