Getting started with MNE Unix command line tools

This tutorial is a really short step by step presentation of an analysis pipeline using the MNE-C command line tools. These tools are UNIX commands and therefore only run on Mac OS or Linux.

See Installing MNE-C to setup your system for using the MNE-C tools.

The quick start guide shows how to run a standard processing of the sample data set provided with MNE. The sample dataset is further described in Datasets.

All the following lines are to be run in a terminal and not in a Python interpreter.

First define your subject:

export SUBJECT=sample

Build your source space:

# MRI (this is not really needed for anything)
mne_setup_mri --overwrite

# Source space
mne_setup_source_space --ico -6 --overwrite

Prepare for forward computation:

# For homogeneous volume conductor (just inner skull)
mne_setup_forward_model --homog --surf --ico 4

# or for a three compartment model (inner and outer skull and skin)
mne_setup_forward_model --surf --ico 4

List your bad channels in a file. Example sample_bads.bad contains:

MEG 2443
EEG 053

Mark bad channels:

mne_mark_bad_channels --bad sample_bads.bad sample_audvis_raw.fif

Compute averaging:

mne_process_raw --raw sample_audvis_raw.fif --lowpass 40 --projoff \
        --saveavetag -ave --ave audvis.ave

Compute the noise covariance matrix:

mne_process_raw --raw sample_audvis_raw.fif --lowpass 40 --projoff \
        --savecovtag -cov --cov audvis.cov

Compute forward solution a.k.a. lead field:

# for MEG only
mne_do_forward_solution --mindist 5 --spacing oct-6 \
    --meas sample_audvis_raw.fif --bem sample-5120 --megonly --overwrite \
    --fwd sample_audvis-meg-oct-6-fwd.fif

# for EEG only
mne_do_forward_solution --mindist 5 --spacing oct-6 \
    --meas sample_audvis_raw.fif --bem sample-5120-5120-5120 --eegonly \
    --fwd sample_audvis-eeg-oct-6-fwd.fif

# for both EEG and MEG
mne_do_forward_solution --mindist 5 --spacing oct-6 \
    --meas sample_audvis_raw.fif --bem sample-5120-5120-5120 \
    --fwd sample_audvis-meg-eeg-oct-6-fwd.fif

Compute MNE inverse operators:

# Note: The MEG/EEG forward solution could be used for all
mne_do_inverse_operator --fwd sample_audvis-meg-oct-6-fwd.fif \
        --depth --loose 0.2 --meg

mne_do_inverse_operator --fwd sample_audvis-eeg-oct-6-fwd.fif \
        --depth --loose 0.2 --eeg

mne_do_inverse_operator --fwd sample_audvis-meg-eeg-oct-6-fwd.fif \
        --depth --loose 0.2 --eeg --meg

Produce stc files (activation files):

# for MEG
mne_make_movie --inv sample_audvis-meg-oct-6-${mod}-inv.fif \
    --meas sample_audvis-ave.fif \
    --tmin 0 --tmax 250 --tstep 10 --spm \
    --smooth 5 --bmin -100 --bmax 0 --stc sample_audvis-meg

# for EEG
mne_make_movie --inv sample_audvis-eeg-oct-6-${mod}-inv.fif \
    --meas sample_audvis-ave.fif \
    --tmin 0 --tmax 250 --tstep 10 --spm \
    --smooth 5 --bmin -100 --bmax 0 --stc sample_audvis-eeg

# for MEG and EEG combined
mne_make_movie --inv sample_audvis-meg-eeg-oct-6-${mod}-inv.fif \
    --meas sample_audvis-ave.fif \
    --tmin 0 --tmax 250 --tstep 10 --spm \
    --smooth 5 --bmin -100 --bmax 0 --stc sample_audvis-meg-eeg

And, we’re done!

See also Command line tools using Python for more command line tools using MNE-Python.