Role of AI in seed science Predictive modelling and Beyond.pptx
MNE group analysis presentation @ Biomag 2016 conf.
1. How to perform MEG group analysis with MNE
MNE software for processing MEG and EEG data, A. Gramfort, M. Luessi, E. Larson, D.
Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, Neuroimage, 2014
MEG and EEG data analysis with MNE-Python,A. Gramfort, M. Luessi, E. Larson, D. Engemann,
D. Strohmeier, C. Brodbeck, R. Goj, M. Jas,T. Brooks, L. Parkkonen, M. Hämäläinen, Frontiers in
Neuroscience, 2013
Biomag 2016
http://www.biomag2016.org/satellite_meetings2.php
http://martinos.org/mne
2. • MNE based on C code developed for ~15 years by MSH
• MNE-Python started ~6 years ago at MGH, Boston
About the project
Source: https://www.ohloh.net/p/MNE
7. MNE People
https://github.com/mne-tools/mne-python/graphs/contributors
Alan Leggitt, Alexander Rudiuk, Alexandre Barachant, Alexandre Gramfort,
Andrew Dykstra, Asish Panda, Basile Pinsard, Brad Buran, Camilo Lamus, Cathy
Nangini, Chris Holdgraf, Christian Brodbeck, Christoph Dinh, Christopher J. Bailey,
Christopher Mullins, Clemens Brunner, Clément Moutard, Dan G. Wakeman,
Daniel McCloy, Daniel Strohmeier, Denis A. Engemann, Emanuele Olivetti, Emily
Ruzich, Emily Stephen, Eric Larson, Fede Raimondo, Federico Raimondo, Félix
Raimundo, Guillaume Dumas, Hafeza Anevar, Hari Bharadwaj, Ingoo Lee, Jaakko
Leppakangas, Jair Montoya, Jean-Remi King, Johannes Niediek, Jona Sassenhagen,
Jussi Nurminen, Kambiz Tavabi, Keith Doelling, Lorenzo De Santis, Louis Thibault,
Luke Bloy, Mads Jensen, Mainak Jas, Manfred Kitzbichler, Manoj Kumar, Marian
Dovgialo, Marijn van Vliet, Mark Wronkiewicz, Marmaduke Woodman, Martin
Billinger, Martin Luessi, Matt Tucker, Matti Hamalainen, Michael Krause, Mikolaj
Magnuski, Natalie Klein, Nick Foti, Nick Ward, Niklas Wilming, Olaf Hauk, Phillip
Alday, Praveen Sripad, Richard Höchenberger, Roan LaPlante, Romain Trachel,
Roman Goj, Ross Maddox, Sagun Pai, Saket Choudhary, Simon Kornblith, Simon-
Shlomo Poil, Sourav Singh, Tal Linzen, Tanay, Teon Brooks, Tom Dupré la Tour,
Yaroslav Halchenko,Yousra Bekhti, Ellen Lau, Mads Jensen !
17. Built on top of FreeSurfer
$ recon-all -s ${SUBJECT} -i xy000000.nii -all
http://surfer.nmr.mgh.harvard.edu/
18. Anatomy workflow
MRI data
MRI data reconstructed
recon-all (Freesurfer 5.1)
BEM mesh
mne watershed_bem
or mne flash_bem
BEM model
MEG/EEG data
Forward solution
or gain matrix
not automatic
needs freesurfer
Coregistration
mne_analyze/mrilab/Python
20. BEM meshes
>>> for subject_id in range(1, 20):
>>> subject = "sub%03d" % subject_id
>>> mne.bem.make_watershed_bem(subject,
>>> subjects_dir=subjects_dir,
>>> overwrite=True)
http://mne-tools.github.io/mne-biomag-group-demo/auto_scripts/11-make_watershed.html
21. Preprocessing: From raw to ERP/ERF
drifts blinks
line
noise
cardiac
10 seconds filtered data clean data using SSPs
Preprocessing
to get clean
evoked data
(ERF/ERP)
or ICA
30. Filter design
High pass filtering removes slow drifts and can make
baselining unnecessary… yet the story is not that simple
http://martinos.org/mne/stable/auto_tutorials/plot_background_filtering.html
46. Much less
fanning
Back to filtering…
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_fanning.html
>>> raw.filter(1, None, l_trans_bandwidth=0.5,
fir_window='hann', phase='zero',
h_trans_bandwidth=‘auto’, filter_length='auto')
47. Covariance est. and whitening
Engemann, D.A., Gramfort,A., Automated model selection in covariance estimation and
spatial whitening of MEG and EEG signals., Neuroimage 2015
>>> cov = mne.compute_covariance(epochs, tmax=0,
method='shrunk')
48. Covariance est. and whitening
>>> cov = mne.compute_covariance(epochs, tmax=0,
method='shrunk')
whitened Global
Field Power
whitened ERF
whitened ERP
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_analysis_1.html
58. Source space group analysis
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_group.html
16 subjects
average of
Contrast
(faces vs.
scrambled