APP UNDER DEVELOPMENT
This app aims at computing the mean transformation matrix across all runs. 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).
Brainlife datatype used:
For input: neuro/meg/fif
For output: neuro/meg/fif
Discuss if these datatypes must be updated.
Inputs of the App:
Files | Format | Description | Optional |
|---|---|---|---|
MEG signal | .fif | first run | No |
MEG signal | .fif | second run | No |
… | .fif | … | Yes |
… | .fif | nth run | Yes |
At least two runs are required.
Output of the App:
Files | Format | Description |
|---|---|---|
Raw file | .fif | Info and data created, contain in |
Steps:
Read all the input files by parsing the
config.jsonExtract 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 “false” data to put in the .fif file
Create raw object
It’s possible to display messages in the Brainlife UI when executing an App: just create a product.json that stores all of the messages (see documentation).
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 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.
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