EEMD

EEMD, or Ensemble Empirical Mode Decomposition, is a data-driven method for decomposing nonlinear and non-stationary signals into a set of intrinsic mode functions (IMFs) and a residual trend. It extends the empirical mode decomposition (EMD) by using an ensemble of noise-assisted analyses to mitigate a problem known as mode mixing, where a single IMF contains signals of different scales.

The typical procedure involves generating an ensemble of signals by adding finite, random white noise to the original data. Each noise-added signal is subjected to EMD to produce IMFs. The IMFs across all ensemble members are then averaged for corresponding modes, yielding the final set of ensemble IMFs. The residual after removing all IMFs represents the trend of the data. EEMD is data-driven and does not rely on predefined basis functions, making it suitable for complex, non-stationary signals.

Advantages and limitations: EEMD can reduce mode mixing and improve robustness to perturbations compared with standard EMD. However, its results depend on choices such as the ensemble size and the amplitude of the added noise, and the method increases computational cost. The ensemble averaging may still influence IMF content depending on parameter settings. Variants such as CEEMD and EEMDAN have been developed to address completeness and residual bias.

Applications: EEMD is widely used in engineering, geophysics, and biomedical signal analysis for denoising, feature extraction, and time-frequency analysis. Common domains include vibration analysis and fault diagnosis, EEG/ECG studies, climate and geophysical data, and analysis of financial time series.