The use of a masking signal to improve empirical mode decomposition

By Ryan Deering, James F. Kaiser. Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

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Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis (2013, neutral): “, at the contact between metal electrodes and skin (via conductive gel); the original tones can be recovered either through a demodulation of IMFs or by using a masking signal [22].”
An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality (2007, methodology): “The reason for this range of frequencies is that the masking signal frequency must be greater than the highest frequency present in the distorted signal [4].”
A Refined Hilbert–Huang Transform With Applications to Interarea Oscillation Monitoring (2009, methodology): “To address the problem of mode mixing, EMD with masking technique was introduced in [27] and [28].”
A time varying filter approach for empirical mode decomposition (2017, methodology): “In order to achieve a better separation performance, specific models, such as linear models [9, 10] and AM-FM models [11-13], are studied.”
The association between working memory precision and the nonlinear dynamics of frontal and parieto-occipital EEG activity (2022, background): “The IMFs serve as a dyadic filter 193 bank to the data (Deering and Kaiser, 2005).”