[Eeglablist] Docs for clustering when dipoles fit with NFT?

[Zeynep Akalin Acar]

NFT does not yet handle transforming sources to average/user-specified head
models. However, as you also noticed there are functions for transforming
points and sources to and from the MNI template to warped template models.
NFT warping calculates and saves forward and backward warping parameters.
This is not sufficient for general purpose use since the affine
transformation parameters are not saved. Let me work on an update that
saves the transformation parameters, and also provide some sample functions
so that you can use it until a more general purpose transformation feature
is implemented.

Thanks,
Zeynep.

On Sun, Jan 27, 2013 at 6:47 PM, Chadwick Boulay
<boulay at bme.bio.keio.ac.jp>wrote:

> Dear Listers,
>
> NFT uses subject-specific MRI data to fit component weights to their
> dipoles but component clustering only makes sense if dipole locations
> are localized in a common (template) brain. I can probably fumble
> through transforming the NFT dipole locations to a template brain but I
> hope that, since this is probably the intended use of NFT, there might
> already be docs or a slide show explaining how to do this. I've scoured
> the SCCN site but I have not found anything.
>
> Any advice?
>
> Thanks,
> Chadwick Boulay
> JSPS Postdoctoral Fellow
> Keio University
> Yokohama, Japan
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