I computed the inverse EEG problem to find the active sources using dSPM.
I plotted scouts using this option: Correlation with sensor .
I want to know if there is some relationship between these sources and ICA method.
Also, I'd like to clarify what you mean by "correlation" here.
Many thanks!
Not at all.
Also, I'd like to clarify what you mean by "correlation" here.
Correlation coefficients between one source signal and one sensor signal, as computed with Matlab's corrcoef. Please see directly in the code for further details:
if (nComponents > 1)
bst_error('Not supported yet for unconstrained sources.', 'Correlation', 0);
return;
elseif (nComponents == 0)
bst_error('Not supported yet for mixed headmodels.', 'Correlation', 0);
return;
end
% ===== COMPUTE CORRELEATION =====
% Compute correlation coefficients for all sources
CorrCoeff = bst_corrn(F, ResultsValues);
% Find maximum correlation value
[MaxCorr, iMaxCorr] = max(abs(CorrCoeff));
% Keep only sources which correlate best
iverts = find(abs(CorrCoeff) >= Threshold);
if isempty(iverts)
iverts = iMaxCorr;
java_dialog('warning', sprintf(['Correlation threshold too high: no source was found matching specified correlation score.' 10 ...
'Adjusting to best correlation found: %1.3f'], MaxCorr), 'Correlation with sensor');
end
% Convert to atlas-based sources if necessary
function [R, pValues] = bst_corrn(X, Y, RemoveMean)
% BST_CORRN: Calculates the same correlation coefficients as Matlab function corrcoef (+/- rounding errors), but in a vectorized way
% Equivalent to bst_correlation with nDelay=1 and maxDelay=0
%
% INPUTS:
% - X: [Nx,Nt], Nx signals varying in time
% - Y: [Ny,Nt], Ny signals varying in time
% - RemoveMean: If 1, removes the average of the signal before calculating the correlation
% If 0, computes a scalar product instead of a correlation
%
% NOTE: The rounding errors
% Corrcoef computes the correlation coefficients based on the variance values computed with cov(),
% instead of a direct sum of the squared values (sum(Xc.^2,2)).
% Hence it uses a corrected algorithm for the computation of the variance, that is not sensible to
% the rounding errors for large number of time samples. We do not divide the values by the number
% of samples here, so if the two signals are the same range of dynamics, those rounding errors
% should not be a problem, even for a very large number of time samples.
% @=============================================================================
% This function is part of the Brainstorm software:
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Okay, thank you very much for your help!