[Eeglablist] question about variance
Phillip M.Gilley
pgilley at utdallas.edu
Thu Jun 23 15:29:31 PDT 2005
Howdy,
When using standard PCA we can easily calculate the percentage of
variance explained from the eigenvalues for each principal component.
Is the calculation similar, or even possible for ICA?
For example, the ICA solution should (theoretically) explain 100% of
the variance in the original data set. However, when looking at the
percentage of variance accounted for (pvaf) from the different
functions in EEGLAB, I see several different types of results. Under
the envtopo() function, there is a specified output for pvaf. This
function calculates the pvaf for each specified component across all
channels; and the calculation appears to be made by subtracting the
variance of the whole data set from the variance of the back projection
of the component. However, it is often the case that the sum of the
values exceeds 100%, which I interpret as the variance in the
distribution of that component irrespective of the contribution of
other components. Is this a correct interpretation? Is there a pvaf
function that returns a set of values for each component for the
original dataset (e.g., for the entire epoch, or for a specified time
range), such that the sum would equal 100%?
Also, the function eeg_pvaf() returns three sets of variances, which
don't necessarily seem to match the pvaf values returned from the
envtopo() function. Are these different calculations altogether? And
if so, what different variances are these values explaining?
Thanks for any insight or comments.
Phillip M. Gilley
The University of Texas at Dallas
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