function [icasig, W]=milca(x,P,kneig,algo,harm,finetune)% Mutual Information based Least Dependent Component Analysis%References:% H. Stogbauer, A. Kraskov, S. A. Astakhov, P.Grassberger, % Phys. Rev. E 70 (6)  066123, 2004% A. Kraskov, H. Stogbauer, P. Grassberger,  % Phys. Rev. E 69 (6) 066138, 2004 % Output: most independent components under linear transformation and the transformation matrix% x....input data MxN   M...channelnummer  N...sampling points  m<<n% P - the number of components to be retained in PCA (estimated number of pure sources)% kneig... k nearest neighbor for MI algorithm% algo ... version of the MI estimator: 1=cubic  2=rectangular% harm ... fit the MI vs. angle curve with # harmonics% finetune ... Number 2^# of angles between 0 and pi/2 (default is 7)[M,N] = size(x);%default-valuesif ~exist('P'), P=M; endif ~exist('kneig'), kneig=12; endif ~exist('algo'), algo=2; endif ~exist('harm'), harm=1; endif ~exist('finetune')    Nb=128;else    Nb=2^finetune;end% PCA - prewhitening + dimension reductioncovX = cov(x'); [E_,D_] = eig(covX);Whitening_mat = inv(sqrtm(D_((M-P+1):M,(M-P+1):M)))*E_(:,(M-P+1):M)'; x=Whitening_mat*x;% save data for external Programmzwsp=x';save zwspmilca.txt zwsp -ASCII% execute C Programm[a unout]=system(['milca zwspmilca.txt ',num2str(P),' ',num2str(N),' ',num2str(kneig),' ',num2str(algo-1),' ',num2str(Nb),' ',num2str(harm)]);Rall=str2num(unout);%outputicasig=Rall*x;W=Rall*Whitening_mat;