# Empirical Mode Decomposition **Data-driven Time-Scale Decomposition Method for Nonlinear and Nonstationary Signals** --- ## What is EMD? - Adaptive signal decomposition technique introduced by Huang et al. (1998) - Decomposes complex signals into simple oscillatory components - No predefined basis functions required - Works directly with the signal's intrinsic characteristics --- ## Intrinsic Mode Functions (IMFs) - Building blocks of EMD decomposition - Each IMF represents a specific time scale - Properties: - Number of extrema and zero-crossings differ by at most one - Local mean is zero at any point - Signal = Sum of IMFs + Residual --- ## Applications - **Biomedical**: EEG/ECG analysis, neural signal processing - **Geophysics**: Seismic data analysis, climate studies - **Engineering**: Vibration analysis, fault detection - **Finance**: Stock market trend analysis - **Oceanography**: Wave analysis --- ## Advantages & Limitations **Advantages:** - Fully adaptive and data-driven - Handles nonlinear and nonstationary signals - No a priori basis functions needed **Limitations:** - Mode mixing issues - Boundary effects - Lack of theoretical foundation - Computational complexity