FieldTrip

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

  MINIMUMNORMESTIMATE computes a linear estimate of the current 
  in a distributed source model  
  
  Use as 
    [dipout] = minimumnormestimate(dip, grad, vol, dat, ...)
 
  Optional input arguments should come in key-value pairs and can include
   'noisecov'         = Nchan x Nchan matrix with noise covariance
   'sourcecov'        = Nsource x Nsource matrix with source covariance (can be empty, the default will then be identity)
   'lambda'           = scalar, regularisation parameter (can be empty, it will then be estimated from snr)
   'snr'              = scalar, signal to noise ratio
   'reducerank'       = reduce the leadfield rank, can be 'no' or a number (e.g. 2)
   'normalize'        = normalize the leadfield
   'normalizeparam'   = parameter for depth normalization (default = 0.5)
   'keepfilter'       = 'no' or 'yes', keep the spatial filter in the
                        output
 
  Note that leadfield normalization (depth regularisation) should be
  done by scaling the leadfields outside this function, e.g. in
  prepare_leadfield.
 
  This implements
  * Dale AM, Liu AK, Fischl B, Buckner RL, Belliveau JW, Lewine JD,
    Halgren E (2000): Dynamic statistical parametric mapping: combining
    fMRI and MEG to produce high-resolution spatiotemporal maps of
    cortical activity. Neuron 26:55-67.
  * Arthur K. Liu, Anders M. Dale, and John W. Belliveau  (2002): Monte
    Carlo Simulation Studies of EEG and MEG Localization Accuracy.
    Human Brain Mapping 16:47-62.
  * Fa-Hsuan Lin, Thomas Witzel, Matti S. Hamalainen, Anders M. Dale,
    John W. Belliveau, and Steven M. Stufflebeam (2004): Spectral
    spatiotemporal imaging of cortical oscillations and interactions
    in the human brain.  NeuroImage 23:582-595.