由Norden E Huang提出的经验模态分解(Empirical Mode Decomposition,即EMD)是一种以信号板值特征尺度为度量的时空滤波过程,其利用信号的上、下包络得到多个分量.这些分量包含了信号从高到低不同频率段的成分,也称固有模态函数,最后的...由Norden E Huang提出的经验模态分解(Empirical Mode Decomposition,即EMD)是一种以信号板值特征尺度为度量的时空滤波过程,其利用信号的上、下包络得到多个分量.这些分量包含了信号从高到低不同频率段的成分,也称固有模态函数,最后的余量为趋势分量,代表信号的平均趋势,这与地球物理场中的区域异常具有相似的意义.根据这一思想,本文提出一种基于EMD的地球物理位场分离方法.与一些常规方法相比,EMD分解结果更接近理论值.鄂东张福山为多年的老矿山,区内标高-400 m以上的铁矿已探明并正在开采,由于人文干扰严重,有效地分离出深部未知铁矿体的异常是该区深部找矿的关键.将EMD用于张福山磁异常分离,并通过对分离后的异常进行2.5D正、反演解释,发现深部铁矿体,表明经验模态分解是一种能有效分离复杂位场的新方法.展开更多
Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provi...Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.展开更多
基金Projects(61201302,61372023,61671197)supported by the National Natural Science Foundation of ChinaProject(201308330297)supported by the State Scholarship Fund of ChinaProject(LY15F010009)supported by Zhejiang Provincial Natural Science Foundation,China
文摘Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.
