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

  DIPOLE_FIT performs an equivalent current dipole fit with a single
  or a small number of dipoles to explain an EEG or MEG scalp topography.
  Use as
    [dipout] = dipole_fit(dip, sens, headmodel, dat, ...)
  Additional input arguments should be specified as key-value pairs and can include
    'constr'      = Structure with constraints
    'display'     = Level of display [ off | iter | notify | final ]
    'optimfun'    = Function to use [fminsearch | fminunc ]
    'maxiter'     = Maximum number of function evaluations allowed [ positive integer ]
    'metric'      = Error measure to be minimised [ rv | var | abs ]
    'checkinside' = Boolean flag to check whether dipole is inside source compartment [ 0 | 1 ]
    'weight'      = weight matrix for maximum likelihood estimation, e.g. inverse noise covariance
  The following optional input arguments relate to the computation of the leadfields
    'reducerank'      = 'no' or number
    'normalize'       = 'no', 'yes' or 'column'
    'normalizeparam'  = parameter for depth normalization (default = 0.5)
  The constraints on the source model are specified in a structure
    constr.symmetry   = boolean, dipole positions are symmetrically coupled to each other
    constr.fixedori   = boolean, keep dipole orientation fixed over whole data window
    constr.rigidbody  = boolean, keep relative position of multiple dipoles fixed
    constr.mirror     = vector, used for symmetric dipole models
    constr.reduce     = vector, used for symmetric dipole models
    constr.expand     = vector, used for symmetric dipole models
    constr.sequential = boolean, fit different dipoles to sequential slices of the data
  The maximum likelihood estimation implements
    Lutkenhoner B. "Dipole source localization by means of maximum
    likelihood estimation I. Theory and simulations" Electroencephalogr Clin
    Neurophysiol. 1998 Apr;106(4):314-21.