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This app aims at computing the mean transformation matrix across all runs of a MEG session to correct the head position of all runs thanks to this matrix. This mean transformation matrix is stored in an empty .fif file, which then can be passed as a parameter in mne.preprocessing.maxwell_filter (destination parameter).
GitHub repository
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For input: neuro/meg/fif
For output: neuro/meg/fif
Discuss if these datatypes must be updated.
Inputs of the App:
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Files | Format | Datatype | Description | Optional |
|---|---|---|---|---|
MEG signal | .fif | meg/fif | first run | No |
MEG signal | .fif | meg/fif | second run | No |
… | … | … | … | Yes |
MEG signal | .fif | meg/fif | nth run | Yes |
| Info |
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At least two runs are required. |
2. The MEG signal we want to preprocess
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Output of the App:
Files | Format | Datatype | Description |
|---|---|---|---|
Raw file | .fif | meg/fif-override |
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Besides, in the output directory, there are the optional files we will use that can be used in a next App and the MEG file we want to preprocess. This is mandatory, because the App that will be run after app-mean-transformation-matrix will can take as input all the files in app-mean-transformation-matrix’s matrix’s output directory corresponding to the datatype of the input of the next App .
Steps:
The python file mean-transformation-matrix.py is composed of several functions:
mean_transformation_matrix()
Extract the transformation matrix from each file
Create info object of the empty .fif file
Compute the mean of all matrices across all files and add it to the info of the empty .fif file
Create fake data to put into the .fif file
Create raw object
main()
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Read all the input files by parsing the config.json
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Raise a ValueError if only one file was given
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Read the MEG data we want to preprocess later and save it
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Read the optional files that will be used in the next preprocessing Apps and, if they exist, copy them in the output directory
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(see How to run Apps one after another).
Parameters of the App
The parameters of the App correspond to the parameters passed to the Python functions used in the App. They are listed in the Their default values proposed in Brainlife or in config.json.example when you test your App locally, then when you register you App on Brainlife, you enter them and a config.json is created by Brainlife.Here, we used the parameters set by default by mne correspond to the default values of MNE Python 0.23.
Next improvements
Rename it into “app-compute-realigment-matrix”?
Allow the user to compute a destination.fif that is not the mean of all the runs but a specific run (this option is available is
mne-bids-pipeline. To do so, it would be best not to save the entire .fif file (i.e. the MEG signals) but to extract theraw.info[“dev_head_t"]["trans"]from this run and put it in an empty fif file, like we do when we compute the mean of the runs (it would save space).Make sure that it is okay to run this app after of before
app-head-pos, the order must not matter.