mne-tools / mne-tools/mne-python
montage / digmontage / transforms documentation
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Description
I was trying to understand how all the transforms work with @teonbrooks . I don't have time to write a full-fledged documentation but I'll jot down the main points -- it could form the skeleton of a tutorial or added to an existing one.
- You have 3 coordinate systems: device, head, and MRI
- The kinds of points you are considering are: hpi (in device space; also called elp when it's in digitizer head space), lpa, rpa, nasion, digitized head shape points
- Goal is to get two transforms: head-device and head-mri, the former is stored in info and the latter is the so-called -trans.fif file that is found by coregistration
- Let's consider the first transform head-device -- it's estimated using HPI coils. You know the HPI location in head space. Then during the recording you emit an RF pulse at 330 Hz (?). This can be filtered and then with dipole fit with a spherical head model, you find the location of these points in device space. Now you have the location in both device and head space, so the dev-head transform can be computed. This is typically done in the acquisition device itself and then read into raw.info['dev_head_transform']
- Now let's consider the second transform: for this you need the lpa, rpa and nasion. It's digitized during the measurement in head space. These are available in raw.info['dig'] and read in using the digitization functions that @massich is working on. Now, you need to mark it in MRI space manually. Once you have the corresponding points, you can coregister them together. Headshape points can be used to further refine the coregistration.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the existing montage, digmontage, and transforms documentation to find where this material belongs. Organize the listed coordinate systems, point types, and head-device/head-MRI workflow into a tutorial or documentation section. Done means the transform process and terminology are clearly explained for readers trying to understand how the transforms work.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100