直接调用eemd就可以啦。下面的eemd程序有注释，不过是英文版的，也不难理解。仔细阅读下你就都会啦。

function allmode=eemd(Y,Nstd,NE)

% This is an EMD/EEMD program

%

% INPUT:

% Y: Inputted data;1-d data only

% Nstd: ratio of the standard deviation of the added noise and that of

% Y; Nstd = (0.1 ~ 0.4)*std(Y).

% NE: Ensemble number for the EEMD, NE = 10-50.

% OUTPUT:

% A matrix of N*(m+1) matrix, where N is the length of the input

% data Y, and m=fix(log2(N))-1. Column 1 is the original data, columns 2, 3, ...

% m are the IMFs from high to low frequency, and comlumn (m+1) is the

% residual (over all trend).

%

% NOTE:

% It should be noted that when Nstd is set to zero and NE is set to 1, the

% program degenerates to a EMD program.(for EMD Nstd=0,NE=1)

% This code limited sift number=10 ,the stoppage criteria can't change.

% References:

% Wu, Z., and N. E Huang (2008),

% Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method.

% Advances in Adaptive Data Analysis. Vol.1, No.1. 1-41.

%

% code writer: Zhaohua Wu.

% footnote:S.C.Su 2009/03/04

%

% There are three loops in this code coupled together.

% 1.read data, find out standard deviation ,devide all data by std

% 2.evaluate TNM as total IMF number--eq1.

% TNM2=TNM+2,original data and residual included in TNM2

% assign 0 to TNM2 matrix

% 3.Do EEMD NE times-----------loop EEMD start

% 4.add noise

% 5.give initial values before sift

% 6.start to find an IMF------IMF loop start

% 7.sift 10 times to get IMF------sift loop start and end

% 8.after 10 times sift --we got IMF

% 9.subtract IMF from data ,and let the residual to find next IMF by loop

% 6.after having all the IMFs-------------IMF loop end

% 9.after TNM IMFs ,the residual xend is over all trend

% 3.Sum up NE decomposition result--------loop EEMD end

% 10.Devide EEMD summation by NE,std be multiply back to data

%% Association: no

% this function ususally used for doing 1-D EEMD with fixed

% stoppage criteria independently.

%

% Concerned function: extrema.m

% above mentioned m file must be put together

%function allmode=eemd(Y,Nstd,NE)

%part1.read data, find out standard deviation ,devide all data by std

xsize=length(Y);

dd=1:1:xsize;

Ystd=std(Y);

Y=Y/Ystd;

%part2.evaluate TNM as total IMF number,ssign 0 to N*TNM2 matrix

TNM=fix(log2(xsize))-1;   % TNM=m

TNM2=TNM+2;

for kk=1:1:TNM2,

for ii=1:1:xsize,

allmode(ii,kk)=0.0;

end

end

%part3 Do EEMD -----EEMD loop start

for iii=1:1:NE, %EEMD loop NE times EMD sum together

%part4 --Add noise to original data,we have X1

for i=1:xsize,

temp=randn(1,1)*Nstd; % add a random noise to Y

X1(i)=Y(i)+temp;

end

%part4 --assign original data in the first column

for jj=1:1:xsize,

mode(jj,1) = Y(jj); % assign Y to column 1of mode

end

%part5--give initial 0to xorigin and xend

xorigin = X1;   %

xend = xorigin; %

%part6--start to find an IMF-----IMF loop start

nmode = 1;

while nmode <= TNM,

xstart = xend; %last loop value assign to new iteration loop

%xstart -loop start data

iter = 1;      %loop index initial value

%part7--sift 10 times to get IMF---sift loop start

while iter<=10,

[spmax, spmin, flag]=extrema(xstart); %call function extrema

%the usage of spline ,please see part11.

upper= spline(spmax(:,1),spmax(:,2),dd); %upper spline bound of this sift

lower= spline(spmin(:,1),spmin(:,2),dd); %lower spline bound of this sift

mean_ul = (upper + lower)/2;            %spline mean of upper and lower

xstart = xstart - mean_ul;              %extract spline mean from Xstart

iter = iter +1;

end

%part8--subtract IMF from data ,then let the residual xend to start to find next IMF

xend = xend - xstart;

nmode=nmode+1;

%part9--after sift 10 times,that xstart is this time IMF

for jj=1:1:xsize,

mode(jj,nmode) = xstart(jj);

end

end

%part10--after gotten all(TNM) IMFs ,the residual xend is over all trend

% put them in the last column

for jj=1:1:xsize,

mode(jj,nmode+1)=xend(jj);

end

%after part 10 ,original + TNM IMFs+overall trend ---those are all in mode

allmode=allmode+mode;

end  %part3 Do EEMD -----EEMD loop end

%part11--devide EEMD summation by NE,std be multiply back to data

allmode=allmode/NE;

allmode=allmode*Ystd;