Noise-assisted MEMD based relevant IMFs identification and EEG classification

Abstract

Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is suitable to analyze multichannel electroencephalography (EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation. For a finite set of multivariate intrinsic mode functions (IMFs) decomposed by NA-MEMD, it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way, and conventional approaches address it by use of prior knowledge. In this work, a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance (JSD) measure. A criterion of effective factor based on JSD was applied to select significant IMF scales. At each decomposition scale, three kinds of JSDs associated with the effective factor were evaluated: between IMF components from data and themselves, between IMF components from noise and themselves, and between IMF components from data and noise. The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.