Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_compute_leadfield”.

```  FT_COMPUTE_LEADFIELD computes a forward solution for a dipole in a a volume
conductor model. The forward solution is expressed as the leadfield
matrix (Nchan*3), where each column corresponds with the potential or field
distributions on all sensors for one of the x,y,z-orientations of the
dipole.

Use as
[lf] = ft_compute_leadfield(dippos, sens, headmodel, ...)
with input arguments
dippos       = position dipole (1*3 or Ndip*3)
sens      = structure with gradiometer or electrode definition
headmodel = structure with volume conductor definition

The headmodel represents a volume conductor model, its contents
depend on the type of model. The sens structure represents a sensor
array, i.e. EEG electrodes or MEG gradiometers.

It is possible to compute a simultaneous forward solution for EEG and MEG
by specifying sens and grad as two cell-arrays, e.g.
sens       = {senseeg, sensmeg}
headmodel  = {voleeg,  volmeg}
This results in the computation of the leadfield of the first element of
sens and headmodel, followed by the second, etc. The leadfields of the
different imaging modalities are subsequently concatenated.

Additional input arguments can be specified as key-value pairs, supported
optional arguments are
'reducerank'      = 'no' or number
'normalize'       = 'no', 'yes' or 'column'
'normalizeparam'  = parameter for depth normalization (default = 0.5)
'weight'          = number or 1xN vector, weight for each dipole position (default = 1)
'backproject'     = 'yes' (default) or 'no', in the case of a rank reduction
this parameter determines whether the result will be
backprojected onto the original subspace

The leadfield weight may be used to specify a (normalized)
corresponding surface area for each dipole, e.g. when the dipoles
represent a folded cortical surface with varying triangle size.

Depending on the specific input arguments for the sensor and volume, this
function will select the appropriate low-level EEG or MEG forward model.
The leadfield matrix for EEG will have an average reference over all the
electrodes.

The supported forward solutions for MEG are
single sphere (Cuffin and Cohen, 1977)
multiple spheres with one sphere per channel (Huang et al, 1999)
realistic single shell using superposition of basis functions (Nolte, 2003)
leadfield interpolation using a precomputed grid
boundary element method (BEM)

The supported forward solutions for EEG are
single sphere
multiple concentric spheres (up to 4 spheres)
leadfield interpolation using a precomputed grid
boundary element method (BEM)

See also FT_PREPARE_VOL_SENS, FT_HEADMODEL_ASA, FT_HEADMODEL_BEMCP,
FT_HEADMODEL_CONCENTRICSPHERES, FT_HEADMODEL_DIPOLI, FT_HEADMODEL_HALFSPACE,
FT_HEADMODEL_INFINITE, FT_HEADMODEL_LOCALSPHERES, FT_HEADMODEL_OPENMEEG,
FT_HEADMODEL_SINGLESHELL, FT_HEADMODEL_SINGLESPHERE,
FT_HEADMODEL_HALFSPACE
```