EMD and EEMD Decomposition Methods in Hilbert-Huang Transform: MATLAB Implementation and Source Code Package from Huang's Research Group

This MATLAB source code package provides comprehensive implementation of EMD (Empirical Mode Decomposition) and EEMD (Ensemble Empirical Mode Decomposition) methods within the Hilbert-Huang Transform framework, developed by Huang's research group. The package includes 44 specialized subroutines, each designed for specific functions in the decomposition process, such as sifting algorithms, intrinsic mode function extraction, and ensemble averaging techniques. The code package enables efficient computation and analysis of EMD and EEMD decompositions for signal processing applications. Key implementation features include adaptive sifting algorithms for IMF extraction, noise-assisted data analysis methods for EEMD, and Hilbert spectral analysis components. Each subroutine contains well-documented parameters and error handling mechanisms to ensure robust performance. Please note that this package requires MATLAB environment and assumes basic programming knowledge along with understanding of signal processing mathematics. We recommend thoroughly reviewing the documentation included in the package to properly understand each subroutine's functionality, input parameters, and output specifications. The code implements advanced numerical algorithms including cubic spline interpolation for envelope estimation and stopping criteria for sifting processes. For optimal usage, users should familiarize themselves with the core EMD/EEMD algorithms and their mathematical foundations. Should you encounter any technical issues or require clarification during implementation, please contact our support team for assistance. We hope this comprehensive MATLAB implementation meets your research and engineering needs, providing reliable tools for nonlinear and non-stationary signal analysis. Enjoy exploring the capabilities of Hilbert-Huang Transform through this extensive code collection.