[Mne_analysis] coordinate frame numbering

Wed Apr 13 09:14:19 EDT 2016
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```Dear Eric
Thank you very much for your response. The coord_frame link is very
handy. I asked those questions because I need to implement a different
inverse problem formulation. For the case of EEG, I can use the
leadfield as given by MNE. However, I need volume source points, and my
question is are the volume source points in the same coordinates as EEG
electrodes???
For the case of the MEG, I do not use the forward solution. I need to
know (a) make sure that the MEG sensors are in the same coordinates as
the volume points and then implement a different formulation. So, some
accessibility to the attributes may be necessary and I just need to be
on top of things, making sure how everything is correct for my end.
many thanks

On 2016-04-13 14:04, Eric Larson wrote:
>> 1. (i) Device coordinate coord_frame=1
>> (ii) Head coordinate coord_frame =2?
>> (iii)Surface RAS coord_frame=3
>> (iv) RAS=4
>
> These values don't match what we have listed:
>
> https://github.com/mne-tools/mne-python/blob/master/mne/io/constants.py#L199
> [1]
>
> Which is:
>
> 1=device
> 2=isotrak
> 3=HPI
> 5=MRI (surface RAS)
>
> But hopefully you don't need to deal with them directly.
>
> It might help to look through some of our transformations code [2] if
> you plan to work with these coordinate frames directly.
>
>> (b) Where is the coil transformation matrix stored(according to
>> the
>> transformation matrix diagrams for different coordinate systems) ?
>
> It's in the `loc` parameter stored as triplets of (r0, ex, ey, ez),
> but again hopefully you can get what you need from some public
> function instead of accessing the attribute directly.
>
>> 4. Sorry for being paranoid!!! But, does the ""Matrix""" below
>> Out[42]:
>> <Transform | MRI (surface RAS)->head>
>> [[ 0.99930958 0.00998476 -0.03578702 -0.00316745]
>> [ 0.01275932 0.81240469 0.5829544 0.00685511]
>> [ 0.03489422 -0.58300841 0.81171643 0.02888404]
>> [ 0. 0. 0. 1. ]]
>> mean that it is the inverse of T2, i.e T2inv???? I ask this because
>> in
>> order to go from Head->Surface RAS, you need the T2 matrix for
>> transformation.
>
> This means that it's a MRI->Head transform, meaning it will take
> triplets in MRI surface RAS space and transform them to the Neuromag
> which you can get by using mne.transforms.invert_transform, which is a
> light wrapper around scipy.linalg.inv. Our forward solution code does
> its calculations in head coordinates, which is why it stores the
> MRI->Head transformation (it's used to transform the BEM surfaces,
> which are natively in MRI RAS coords).
>
> Eric
>
>
>
> ------
> [1]
> https://github.com/mne-tools/mne-python/blob/master/mne/io/constants.py#L199
> [2]
> https://github.com/mne-tools/mne-python/blob/master/mne/transforms.py
>
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--
best regards