Empirical Mode Decomposition

Empirical Mode Decomposition (EMD) is a signal processing technique used to decompose complex, non-stationary signals into simpler, intrinsic mode functions (IMFs), revealing underlying patterns and trends. Current research focuses on enhancing EMD's robustness and efficiency, often by integrating it with machine learning algorithms like support vector machines (SVMs), neural networks, and deep learning architectures such as recurrent convolutional neural networks (RRCNNs), to improve signal denoising, feature extraction, and prediction accuracy. This approach finds applications in diverse fields, including EEG signal analysis, wind speed forecasting, and human behavior recognition, improving the accuracy and efficiency of data analysis and prediction models.