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{{tag>faq eeg meg headmodel}} ====== Can I do combined EEG and MEG source reconstruction? ====== In principle the answer is "yes". However, it is sofar only supported by the low-level code in [[:development:forwinv]] and not by the high-level FieldTrip functions such as **[[:reference:dipolesimulation]]**, **[[:reference:dipolefitting]]** and **[[:reference:sourceanalysis]]**. Below is an example that demonstrates how forward computations can be done. Inverse source reconstructions using the low-level code should work similar, i.e. by combining the eeg and meg sensor definitions and volume conduction models into a cell array. Note that the same approach can also be used for combined EEG and ECoG, or combined MEG and ECoG or any other data fusion. <code> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % create a set of electrodes, randomly placed on the sphere elec = []; elec.pnt = randn(32,3); dum = sqrt(sum(elec.pnt.^2,2)); elec.pnt = elec.pnt ./ [dum dum dum]; % scale them to a unit sphere for i=1:32 elec.label{i} = sprintf('eeg%03d', i); end % create a concentric 3-sphere volume conductor for the EEG, the radius is the same as for the electrodes vol_eeg = []; vol_eeg.r = [0.88 0.92 1.00]; % radii of spheres vol_eeg.c = [1 1/80 1]; % conductivity vol_eeg.o = [0 0 0]; % center of sphere %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % create a set of magnetometers, randomly placed around the sphere grad = []; grad.pnt = randn(64,3); dum = sqrt(sum(grad.pnt.^2,2)); grad.pnt = grad.pnt ./ [dum dum dum] * 1.2; % scale them to a unit sphere and shift outward a bit grad.ori = grad.pnt ./ [dum dum dum]; % unit length for i=1:64 grad.label{i} = sprintf('meg%03d', i); end grad.tra = eye(64,64); % create a single-sphere volume conductor for the MEG vol_meg = []; vol_meg.r = 1.00; % radius of sphere vol_meg.c = 1; % conductivity vol_meg.o = [0 0 0]; % center of sphere %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % combine the EEG and MEG sensor definitions and volume conductor models % and do a forward computation combined_vol = {vol_eeg, vol_meg}; combined_sens = {elec, grad}; pos = [0 0 0.8]; mom = [1 0 0]'; [combined_vol{1}, combined_sens{1}] = prepare_vol_sens(combined_vol{1}, combined_sens{1}); [combined_vol{2}, combined_sens{2}] = prepare_vol_sens(combined_vol{2}, combined_sens{2}); leadfield = compute_leadfield(pos, combined_sens, combined_vol) * mom; whos leadfield Name Size Bytes Class Attributes leadfield 96x1 768 double figure; plot(leadfield(1:32)); title('eeg'); figure; plot(leadfield(33:end)); title('meg'); </code> The combination of volume conduction models can of course also contain more realistically and accurate forward models.