关键词: 经验模态分解, 端点效应, 延拓, 支持向量机, 窗函数, 正交性

Abstract: In order to restrain the problem of end effect in the Empirical Mode Decomposition （EMD） process of the EEG data, a new method is proposed. This method combines Support Vector Machine （SVM） and window function to deal with the original signal. The method consists of three steps: the signal is forward and backward extended respectively for several extreme points by SVM; window function is added to the extended data; the empirical mode decomposition is used to process the original signal, and the extended signal by the proposed method respectively, and then remove the extended points from the intrinsic mode functions. In order to analyze the performance of the proposed method, orthogonality is chosen as the quantitative evaluation index. The artificial and real EEG signals are used as experimental objects of simulation experiments. Comparing with the other methods, the experimental resluts show that the proposed method can effectively restrain the end effects during the process of empirical mode decomposition.

Key words: empirical mode decomposition, end effects, continuation, support vector machines, window function, orthogonality