Update projector structure. And add ICA explained variance

[rcassani]

This PR adds code to compute the variance explained by the IC when using ICA decomposition.

The reasonable place to store these values is the SingVal field in the projector structure. However, this changes for back compatibility are needed as this field is currently either the PCA explained variance, or a string to identify other projectors ICA or REF (montage).

brainstorm3/toolbox/process/functions/process_ssp2.m

Lines 947 to 952 in e532c6d

	% There are 5 categories of projectors:
	% - SSP_pca: CompMask=[Ncomp x 1], SingVal=[Ncomp x 1], Components=[Nchan x Ncomp]=U
	% - SSP_mean: CompMask=1, SingVal=[], Components=[Nchan x 1]=U
	% - ICA: CompMask=[Ncomp x 1], SingVal='ICA', Components=[Nchan x Ncomp]=W'
	% - REF: CompMask=[], SingVal='REF', Components=[Nchan x Ncomp]=Wmontage
	% - Other: CompMask=[], SingVal=[], Components=[Nchan x Nchan]=Projector=I-UUt

Tentative solution:
1. Use the Comment to identify the type of projector
2. Always require the projector Comment to have the type of projector on it

Implemented solution:

• Add field 'Method' to 'Projector' structure to identify Projector
• Support projectors without 'Method'. Get projector method from its content
• Store ICA explained variance in SingVal

References:
mne-tools/mne-python#11141

---

Being F the original data, the independent components Y can be obtained by multiplying the unmixing matrix W with F. Then we can mix those ICs Y with the mixing matrix M (which is the inverse of W) to recover the observed data F this is to say F'

Y = W * F
F' = M * Y

Then we can compute the variance explained by each IC as:

varIcs = sum(M.^2, 1) .* transpose(sum(Y.^2, 2))
varIcs = varIcs / sum(varIcs)

However this is the explained variance of F', so these explained variances need to be scaled with ratio of variance between F' and F.

Fdiff = (F - M * Y)
rVarExp = 1 - (sum(sum(Fdiff.^2, 2)) ./ sum(sum(F.^2, 2)))

Variance in original data by each component
varIcs = rVarExp * varIcs