Empirical Mode Decomposition (EMD) Algorithm Toolbox

This documentation introduces the installation and usage examples of the Empirical Mode Decomposition (EMD) algorithm toolbox. EMD is a signal decomposition and analysis technique specifically designed for processing nonlinear and non-stationary signals. The core algorithmic objective involves decomposing signals into multiple Intrinsic Mode Functions (IMFs), where each IMF captures distinct frequency and amplitude characteristics through an iterative sifting process. From an implementation perspective, the EMD algorithm typically utilizes envelope detection and cubic spline interpolation to extract local signal features effectively. The toolbox provides MATLAB/Python functions that implement key components including: signal preprocessing routines, IMF extraction algorithms with stopping criteria configuration, and visualization modules for result analysis. This decomposition technique enables robust local feature extraction from complex signals, making it widely applicable in signal processing, image analysis, and data analytics domains. Understanding the toolbox architecture and implementation workflow is therefore essential for researchers and engineers working with adaptive signal decomposition methods. The package includes configuration files for dependency management and demo scripts demonstrating complete processing pipelines from raw input to IMF components.