FT_PREPARE_LEADFIELD computes the forward model for many dipole locations
 on a regular 2D or 3D grid and stores it for efficient inverse modelling

 Use as
   [grid] = ft_prepare_leadfield(cfg, data);

 It is neccessary to input the data on which you want to perform the
 inverse computations, since that data generally contain the gradiometer
 information and information about the channels that should be included in
 the forward model computation. The data structure can be either obtained
 from FT_PREPROCESSING, FT_FREQANALYSIS or FT_TIMELOCKANALYSIS. If the data is empty,
 all channels will be included in the forward model.

 The configuration should contain
   cfg.channel            = Nx1 cell-array with selection of channels (default = 'all'),
                            see FT_CHANNELSELECTION for details

 The positions of the sources can be specified as a regular 3-D
 grid that is aligned with the axes of the head coordinate system
   cfg.grid.xgrid      = vector (e.g. -20:1:20) or 'auto' (default = 'auto')
   cfg.grid.ygrid      = vector (e.g. -20:1:20) or 'auto' (default = 'auto')
   cfg.grid.zgrid      = vector (e.g.   0:1:20) or 'auto' (default = 'auto')
   cfg.grid.resolution = number (e.g. 1 cm) for automatic grid generation
 Alternatively the position of a few sources at locations of interest can
 be specified, for example obtained from an anatomical or functional MRI
   cfg.grid.pos        = N*3 matrix with position of each source
   cfg.grid.inside     = N*1 vector with boolean value whether grid point is inside brain (optional)
   cfg.grid.dim        = [Nx Ny Nz] vector with dimensions in case of 3-D grid (optional)

 The volume conduction model of the head should be specified as
   cfg.headmodel     = structure with volume conduction model, see FT_PREPARE_HEADMODEL

 The EEG or MEG sensor positions can be present in the data or can be specified as
   cfg.elec          = structure with electrode positions, see FT_DATATYPE_SENS
   cfg.grad          = structure with gradiometer definition, see FT_DATATYPE_SENS
   cfg.elecfile      = name of file containing the electrode positions, see FT_READ_SENS
   cfg.gradfile      = name of file containing the gradiometer definition, see FT_READ_SENS

 Optionally, you can modify the leadfields by reducing the rank (i.e.
 remove the weakest orientation), or by normalizing each column.
   cfg.reducerank      = 'no', or number (default = 3 for EEG, 2 for MEG)
   cfg.normalize       = 'yes' or 'no' (default = 'no')
   cfg.normalizeparam  = depth normalization parameter (default = 0.5)
   cfg.backproject     = 'yes' or 'no' (default = 'yes') determines when reducerank is applied
                         whether the lower rank leadfield is projected back onto the original
                         linear subspace, or not.

 Depending on the type of headmodel, some additional options may be
 specified. 

 For OPENMEEG based headmodels:
   cfg.openmeeg.batchsize    = scalar (default 100e3), number of dipoles 
                               for which the leadfield is computed in a 
                               single call to the low-level code. Trades off
                               memory efficiency for speed.
   cfg.openmeeg.dsm          = 'no'/'yes', reuse existing DSM if provided
   cfg.openmeeg.keepdsm      = 'no'/'yes', option to retain DSM (no by default)
   cfg.openmeeg.nonadaptive  = 'no'/'yes'
  
 For SINGLESHELL based headmodels:
   cfg.singleshell.batchsize = scalar or 'all' (default 1), number of dipoles
                               for which the leadfield is computed in a 
                               single call to the low-level code. Trades off
                               memory efficiency for speed.

 To facilitate data-handling and distributed computing you can use
   cfg.inputfile   =  ...
 If you specify this option the input data will be read from a *.mat
 file on disk. This mat files should contain only a single variable named 'data',
 corresponding to the input structure.

 See also FT_SOURCEANALYSIS, FT_DIPOLEFITTING, FT_PREPARE_HEADMODEL,
 FT_PREPARE_SOURCEMODEL