Programs for EMD, EEMD, and CEEMD Algorithms

This collection features Gabriel Rilling's MATLAB implementations of Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Complementary Ensemble Empirical Mode Decomposition (CEEMD). The package includes practical examples and accompanying research literature. These algorithms enable researchers to perform more accurate and efficient analyses in signal processing and vibration analysis domains. EMD serves as an adaptive time-frequency analysis method that decomposes complex signals into Intrinsic Mode Functions (IMFs) through an iterative sifting process. EEMD enhances the original EMD by incorporating noise-assisted analysis to mitigate mode mixing issues, while CEEMD further improves upon EEMD by using complementary noise pairs to reduce residual noise and computational costs. The implementations feature robust boundary handling and stopping criteria mechanisms to ensure proper IMF extraction. These programs provide deeper insights into signal characteristics and structural components, forming a fundamental toolkit for advanced research and practical applications in nonlinear and non-stationary signal analysis. The code organization follows modular design principles with clear function interfaces for easy integration into larger processing pipelines.