we konw that to reduce the number of time series in nalysing EEG data it is better to use PCA what is mathematical approach to use PCA?
Extract eigenvalues and eigenvectors of the correlation matrix. The eigenvectors are the PCs. See the links below. Best wishes, David
http://www.bing.com/search?q=principal+component+analysis&qs=LS&pq=principal+c&sc=8-11&cvid=AC37F8291A724637AA947DC6CBC90864&FORM=QBRE&sp=2&ghc=1
http://www.bing.com/search?q=principal+component+analysis+EEG&qs=n&form=QBRE&sp=-1&pq=principal+component+analysis+eeg&sc=8-32&sk=&cvid=1D4FB1318C114F219D1EEABCAEC919A1
Hi,
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It's often used to make data easy to explore and visualize.
PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.
For more information see the following links:
http://www.utdallas.edu/~herve/abdi-awPCA2010.pdf
http://www.math.union.edu/~jaureguj/PCA.pdf
http://setosa.io/ev/principal-component-analysis/
Hope this helps.
My suggestion: https://www.researchgate.net/publication/319469038_New_Interpretation_of_Principal_Components_Analysis
Article New Interpretation of Principal Components Analysis
Dear Hossein
This tutorial (attached file)l is designed to give the reader an understanding of Principal Components Analysis (PCA).
Here is a very good source of PCA including presentation and source code Presentation Principal Component Analysis (PCA) : An Overview Article Principal component analysis - a tutorial Alaa
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