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reference:minimumnormestimate [2018/08/23 14:43]
reference:minimumnormestimate [2018/08/23 14:43] (current)
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 +=====  MINIMUMNORMESTIMATE =====
 +
 +Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help minimumnormestimate"​.
 +
 +<​html><​pre>​
 +  <a href=/​reference/​minimumnormestimate><​font color=green>​MINIMUMNORMESTIMATE</​font></​a>​ computes a linear estimate of the current in a
 +  distributed source model.
 + 
 +  Use as
 +    [dipout] = minimumnormestimate(dip,​ grad, headmodel, dat, ...)
 + 
 +  Optional input arguments should come in key-value pairs and can include
 +    '​noisecov' ​        = Nchan x Nchan matrix with noise covariance
 +    '​noiselambda' ​     = scalar value, regularisation parameter for the noise covariance matrix (default = 0)
 +    '​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
 +    '​prewhiten' ​       = '​no'​ or '​yes',​ prewhiten the leadfield matrix with the noise covariance matrix C
 +    '​scalesourcecov' ​  = '​no'​ or '​yes',​ scale the source covariance matrix R such that trace(leadfield*R*leadfield'​)/​trace(C)=1
 + 
 +  Note that leadfield normalization (depth regularisation) should be done
 +  by scaling the leadfields outside this function, e.g. in
 +  prepare_leadfield. Note also that with precomputed leadfields the
 +  normalization parameters will not have an effect.
 + 
 +  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.
 +</​pre></​html>​