关键词: 经验模态分解, 数据延拓, 镜面延拓, 端点效应

Abstract: The Empirical Mode Decomposition （EMD） is an advanced method for signal analysis. Especially, it has a significant effect on EEG and other nonlinear and non-stationary signals. But there is an involved end issue in the courage of getting two envelops of the data using spline interpolation. On the basis of existing algorithms for solving the problem, a new method is proposed. Generally, it fits the sequence near the end with a fitting function, and then extends a extreme point outside the endpoint with the fitting function. Finally, it utilizes the mirror extension method to realize the process of EMD algorithm. For the extended data, the spline does not swing at both ends of the data. The experimental results prove that the new method can decompose EEG effectively.

Key words: Empirical Mode Decomposition（EMD）, data extending, mirror extension, ending effect