FastICA_ICA_matlab_EEG_单通道FASTICA_fastICA单通道

function fuzzyen = Fuzzy_Entropy( dim, r, data, tau ) % FUZZYEN Fuzzy Entropy % calculates the fuzzy entropy of a given time series data % Similarity definition based on vectors' shapes, together with the % exclusion of self-matches, earns FuzzyEn stronger relative consistency % and less dependence on data length. % dim : embedded dimension % r : tolerance (typically 0.2 * std) % data : time-series data % tau : delay time for downsampling (user can omit this, in which case % the default value is 1) if nargin < 4, tau = 1; end if tau > 1, data = downsample(data, tau); end N = length(data); Phi = zeros(1,2); for m = dim:dim+1 Ci = zeros(1,N-m+1); dataMat = zeros(m,N-m+1); % setting up data matrix - form vectors for j = 1:m dataMat(j,:) = data(j:N-m+j); end % baseline U0 = mean(dataMat); % remove baseline and calculate the absolute values Sm = abs(dataMat - repmat(U0,m,1)); % Given vector Si, calculate the similarity degree between its' % neighboring vector Sj for i = 1:N-m+1 Sm_tmp = Sm; Sm_tmp(:,i) = []; % excluded self-matches % maximum absolute difference of the corresponding scalar components % of Si and Sj (j≧i) dij = max(repmat(Sm(:,i),1,N-m) - Sm_tmp); % similarity degree Aij = exp(-log(2)*(dij/r).^2); % averaging all the similarity degree of its neighboring vectors Sj Ci(i) = sum(Aij)/(N - m); end % summing over the counts Phi(m-dim+1) = sum(Ci)/(N-m+1); % 耳_m and 耳_m+1 end fuzzyen = log(Phi(1))-log(Phi(2)); % fuzzyen = ln(耳_m)-ln(耳_m+1) end