EEMD Algorithm: An Enhanced Version Based on EMD with MATLAB Implementation

In this documentation, we present the MATLAB implementation of the Ensemble Empirical Mode Decomposition (EEMD) algorithm, an enhanced version developed by Huang in 2009 building upon the foundation of Empirical Mode Decomposition (EMD). This algorithm was specifically designed to improve signal data processing through advanced decomposition techniques. The core methodology employs Empirical Mode Decomposition (EMD), which systematically breaks down signals into multiple Intrinsic Mode Functions (IMFs) representing oscillatory components at different frequency scales. The EEMD enhancement introduces a noise-assisted approach where multiple trials with added white noise are performed, followed by ensemble averaging of the resulting IMFs. This process significantly improves the algorithm's accuracy and stability by mitigating mode mixing issues present in standard EMD. The MATLAB implementation includes key functions for signal preprocessing, noise injection parameters, iterative decomposition cycles, and IMF extraction routines. Users can customize parameters such as noise amplitude, ensemble size, and stopping criteria for the sifting process. The code structure facilitates easy integration with various signal types including audio waveforms, image data, and sensor readings. Developed originally in 2009 by Huang and refined through years of practical application, this implementation has been extensively validated across numerous signal processing scenarios. The code provides a robust framework for applying EEMD to analyze and process diverse signal datasets, offering improved decomposition quality compared to traditional EMD approaches. We anticipate this implementation will serve as a valuable tool for your signal processing requirements.