Pull Request from Melanie Zhao

Ex01_testPCA.m

@@ -1,5 +1,6 @@
 % Principal component analysis
 %
+% Melanie Zhao: [email protected]
 % BMI500 Course
 % Lecture:  An Introduction to Blind Source Separation and Independent Component Analysis
 %           By: R. Sameni
@@ -36,3 +37,48 @@
 N = size(x, 1); % The number of channels
 T = size(x, 2); % The number of samples per channel
 
+% plot the channels
+% eog1 eog2 eog3-23 24 25 
+%ecg 26 
+PlotECG(x,4,'b',fs,'Raw Data Channel')
+
+% remove the channel means
+x_demeaned=x-mean(x,2) * ones(1,size(x,2));
+
+Cx = cov(x_demeaned');
+
+% eIGENVALUE DECOMPOSITION
+[V,D]=eig(Cx,'Vector');
+
+
+x_var = var(x_demeaned,[],2);
+x_var2=diag(Cx);
+
+y=V' * x_demeaned;
+Cy= cov(y');
+y_var = diag(Cy);
+
+
+x_total_energy = sum(x_var)
+Cx_trace=trace(Cx)
+eigenvale_sum=sum(D)
+Cy_trace = trace(Cy)
+
+x_partial_energy = 100.0 * cumsum(D(end:-1:1))./x_total_energy
+
+
+th=99.9;
+
+N_eigs_to_keep = find(x_partial_energy <= th,1,'last')
+
+
+% first 19 eigenvalue, re compress the x
+
+
+
+x_compressed = V(:,N-N_eigs_to_keep +1 :N)* y(N-N_eigs_to_keep + 1 :N,:)
+
+
+
+
+

Ex02_testEigenAnalysisPowerMethod.m

@@ -24,3 +24,4 @@
 % Cx = x * x';
 Cx = cov(x');
 
+% check on eigen value decomposition work