Tags: example eeg meg headmodel source

Combined EEG and MEG source reconstruction


This example script shows how to do combined EEG and MEG source reconstruction. It is sofar only supported by the low-level code in forwinv and not by the high-level FieldTrip functions such as ft_dipolesimulation, ft_dipolefitting and ft_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 invasive EEG, or combined MEG and invasive EEG, or any other data fusion. Furthermore note that the combination of volume conduction models can contain more realistically and accurate forward models than those used below.

MATLAB script

% 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);

% 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.coilpos = randn(64,3);
dum = sqrt(sum(grad.coilpos.^2,2));
grad.coilpos = grad.coilpos ./ [dum dum dum] * 1.2;  % scale them to a unit sphere and shift outward a bit
grad.coilori = grad.coilpos ./ [dum dum dum];        % unit length
for i=1:64
grad.label{i} = sprintf('meg%03d', i);
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}] = ft_prepare_vol_sens(combined_vol{1}, combined_sens{1});
[combined_vol{2}, combined_sens{2}] = ft_prepare_vol_sens(combined_vol{2}, combined_sens{2});
leadfield   = ft_compute_leadfield(pos, combined_sens, combined_vol) * mom;

figure; plot(leadfield(1:32)); title('eeg');
figure; plot(leadfield(33:end)); title('meg');

whos leadfield

Name            Size            Bytes  Class     Attributes
leadfield      96x1               768  double

>> senstype(combined_sens)

ans =
  'electrode'    'meg'

>> ft_voltype(combined_vol)

ans =
  'concentric'    'singlesphere'