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