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APP UNDER DEVELOPMENT

This app aims at computing the mean transformation matrix across all runs 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

https://github.com/AuroreBussalb/app-mean-transformation-matrix

Brainlife datatype used:

Discuss if these datatypes must be updated.

Datatype updated, see Datatype meeting 18.02.2021

Inputs of the App:

Files

Format

Description

Optional

MEG signal

.fif

first run

No

MEG signal

.fif

second run

No

…

…

…

Yes

MEG signal

.fif

nth run

Yes

At least two runs are required.

Output of the App:

Files

Format

Description

Raw file

.fif

  • Info and fake data created

  • the mean transformation matrix is contained in raw.info["dev_head_t"]["trans"]

Steps:

The python file mean-transformation-matrix.py is composed of several functions:

  • mean_transformation_matrix()

  1. Extract the transformation matrix from each file

  2. Create info object of the empty .fif file

  3. Compute the mean of all matrices across all files and add it to the info of the empty .fif file

  4. Create fake data to put into the .fif file

  5. Create raw object

  • main()

  1. Read all the input files by parsing the config.json

  2. Raise a ValueError if only one file was given

  3. Apply mean_transformation_matrix()

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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