MNE BIDS MEG Anonymization

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Introduction to Import/Upload Process

Datasets must be organized prior to anonymization.
Several organizational options will be provided to assist in recognizing and linking the datasets.
There are a variety of options ranging from extensive renaming of files to manipulating search expressions (choose the option most appropriate for your lab) Prior to anonymization - a "master_list" will be created linking the appropriate input information.

Setup Container

 #This installs mne_docker / mne-bids / mne-bids-pipeline
 git clone git@tako:mne_singularity
 cd mne_singularity 
 sudo singularity build singularity_build.sif singularity_build.def 

Link freesurfer or build from singularity:

 ln -s PATH_TO_FREESURFER ./freesurfer

Create Head Surface to view anonymization


Data Curating before anonymization

This data curation will be important to generate a Master_List.csv file that will be used to perform the anonymization.
The Master_List.csv will link the subject MRI, subject MEG Rest Dataset, MEG EmptyRoom Dataset, coreg transform.
The Master_List.csv IS NOT ANONYMIZED, so do not share with the NIH or other outside groups.
After the Master_List.csv is generated, Step 2 will validate the Master_List and return errors/warnings when data cannot be found.
If a few errors are generated, these can be fixed directly on the Master_List.csv. If most/all of the datasets throw errors, the data curation may need to be changed.


This option requires the most space/manual labor, but is easy to troubleshoot

Copy all data into a single folder using the following format:
All MRIs must be of .nii or .mgz
All MEG datasets must be single files or directories: .ds, .fif, .sqd, 4D-dataset (?)

 MRIs and MEG datasets must have the same prefix.   
 EG - Subj1.ds  Subj1.nii   OR  LastName_FirstName.fif  LastName_FirstName.nii  


Create a link file (csv format) that links the MEG subjectID to the MRI subjectID
Provide the top level MEG path
Provide the top level MRI path


Manually generate the Master_List.csv by hand using the template csv.
Troubleshooting can be tedious due to typing errors in the path.


This option requires the most knowledge to troubleshoot string parsing, but is the most flexible

Using a dictionary enter the search strings to extract the meg_path, mri_path, subjid etc: