GaussSmoothKernel

Calculates a simple, discrete Gaussian smoothing kernel of a specifice size given the covariance matrix of the Gaussian.

 noise 

 low-level

Generates functional Magnetic Resonance Imaging (fMRI)
time series or 4D data. Some high-level
functions are created for fast data generation with only
a few arguments and a diversity of functions to define
activation and noise. For more advanced users it is possible
to use the low-level functions and manipulate the arguments.
See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.

Karsten Tabelow

neuRosim

Simulate fMRI Data

Marijke Welvaert

Joke Durnez

Beatrijs Moerkerke

Yves Rosseel

Geert Verdoolaege

GaussSmoothKernel function

<dl> <dt>voxdim</dt>
<dd>The dimensions of each voxel.</dd> <dt>ksize</dt>
<dd>The size (in voxels) of the kernel with which to filter the independent field.</dd> <dt>sigma</dt>
<dd>The covariance matrix of the Gaussian kernel.</dd></dl>

Arguments

Author

Calculates a discrete Gaussian smoothing kernel (adopted from AnalyzeFMRI) — GaussSmoothKernel

<dl>

 <dt>voxdim</dt>
<dd>The dimensions of each voxel.</dd>

 <dt>ksize</dt>
<dd>The size (in voxels) of the kernel with which to filter the independent field.</dd>

 <dt>sigma</dt>
<dd>The covariance matrix of the Gaussian kernel.</dd>

</dl>

Calculates a discrete Gaussian smoothing kernel (adopted from AnalyzeFMRI)

GaussSmoothKernel: Calculates a discrete Gaussian smoothing kernel (adopted from AnalyzeFMRI)

Description

Usage

Value

Arguments

Author

See Also

Examples