MNE Python Tutorial 2021

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Steps to prepare prior to tutorial

Prepare python environment - For 3D rendering and interactive plots, this should be installed on your local computer

 !!IF YOU DO NOT HAVE MINICONDA/ANACONDA INSTALLED -  have IT install miniconda under your user account!!
 !!If you already have an mne environment, you can use another name for the environment and adjust accordingly!!
 
 conda activate base 
 conda install -n base mamba -c conda-forge -y  
 mamba create -n mne conda-forge::mne main::pip main:jupyter -y
 conda activate mne

Download the sample data and scripts.

The data is available from the resources below. A final jupyter notebook may be sent over email prior to the tutorial

NIMH Users: Download here

 scp <USERNAME>@helix.nih.gov:/data/NIMH_scratch/mne_tutorial/mne_tutorial.tar.gz ./
 tar -xvf mne_tutorial.tar.gz

Non-NIMH MEG Users: Download from MEG Data server

 The data is located on the MEG data server and is accessible by all users with accounts
 An email will be sent with the path to the data

Start Jupyter Notebook

 cd <NEW PATH>/nimh_tutorial_data
 jupyter notebook mne_tutorial_10_01_21.ipynb

Prep Non-tutorial Data

 # Run Freesurfer on Dataset (takes 8+ hrs): 
 # Does not run on BRIK/HEAD - convert to .nii first   -- e.g (module load afni; 3dAFNItoNIFTI  anat+orig. )
 
 # Install and process freesurfer locally on computer https://surfer.nmr.mgh.harvard.edu/fswiki/rel7downloads
 recon-all -all -i <MRI Used w/ MEG> -s <SUBJECT>
 
 #INSTRUCTIONS FOR BIOWULF
 subjid=       #Set Freesurfer ID
 mri=          #Set MRI Name - must be a nifti file NOT BRIK/HEAD
 export SUBJECTS_DIR=      #Set output folder, Make sure this directory exists
 module load freesurfer
 echo -e '#!/bin/bash\nrecon-all -all  -i ' ${mri} -s ${subjid} | sbatch  --mem=3g --time=24:00:00     #SUBMITS JOB TO SBATCH
 
 #In bash terminal - process the boundary element model - also requires freesurfer to be installed
 subjid=<SUBJECT>
 subjects_dir=<SUBJECTS_DIR>
 conda activate mne
 python -c "import mne; mne.bem.make_watershed_bem(subject='${subjid}', subjects_dir='${subjects_dir}')"

More info to come soon!