MATLAB Implementation of EMD Algorithm with Mirror Extension

The EMD (Empirical Mode Decomposition) algorithm is a signal processing method based on mirror extension technique. This method decomposes signals into different Intrinsic Mode Functions (IMFs) and residual trends. The core principle involves iteratively calculating the minimal distance between the original signal and its mirror-extended version to determine the signal's intrinsic mode functions. In MATLAB implementation, key steps include: 1. Mirror extension of signal boundaries to handle edge effects 2. Iterative sifting process to extract IMFs through local extrema identification 3. Hilbert-Huang transform implementation for instantaneous frequency analysis 4. Stopping criteria based on standard deviation between consecutive sifting results The algorithm employs functions like: - findpeaks() for local maxima/minima detection - spline interpolation for envelope construction - IMF validation using the Cauchy convergence criterion This method is widely applied in signal processing and analysis, enabling deeper understanding of signal characteristics and trends through adaptive time-frequency decomposition. The mirror extension approach significantly reduces boundary effects compared to traditional EMD implementations.