Web site is at: http://mne-tools.github.io/mne-biomag-group-demo/

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

3. http://martinos.org/mne/

4. http://mne-tools.github.io/mne-biomag-group-demo/

5. People behind this work
@agramfort @dengemann
@jasmainak
http://martinos.org/mne/stable/whats_new.html
@jaeilepp
@Eric89GXL

6. (Some) MNE People
@agramfort @mluessi @dengemann
@lauriparkkonen
@Eric89GXL
@mshamalainen
@t3on
@joewalter
@jasmainak
@rgoj
@chris=anmbrodbeck
@jaeilepp
@adykstra @leggi@a
@kazemakase
@TalLinzen
@OlafHauk @ jdammers
http://martinos.org/mne/stable/whats_new.html
@kingjr

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 !

8. https://github.com/mne-tools/mne-biomag-group-demo/
Get the codeto replicate

9. http://mne-tools.github.io/mne-biomag-group-demo/auto_scripts/index.html
Analysis Scripts

10. One script

12. .../mne-biomag-group-demo/tree/master/scripts/processing
Fetch data from OpenfMRI
Sensor space analysis
Source space analysis
Anatomical pipeline

13. http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/index.html
Results atgroup level

14. http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/index.html
Demos
and results for each
subject

15. Installing MNE-Python
$ pip install --upgrade --user mne
Install Scientific Python environment, e.g.,Anaconda at
http://martinos.org/mne/stable/install_mne_python.html
https://www.continuum.io/downloads
Install MNE-Python:

16. .../mne-biomag-group-demo/tree/master/scripts/processing
Fetch data from OpenfMRI
Sensor space analysis
Source space analysis
Anatomical pipeline

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

19. BEM meshes
http://martinos.org/mne/stable/auto_tutorials/plot_forward.html

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

22. Maxwell filtering
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_maxfilter.html
MNE implements
maxfilter (TM)

23. Maxwell filtering
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_maxfilter.html
MNE implements
maxfilter (TM)

24. Maxwell filtering
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_maxfilter.html
MNE implements
maxfilter (TM)

25. .../mne-biomag-group-demo/tree/master/scripts/processing
Fetch data from OpenfMRI
Sensor space analysis
Source space analysis

26. Filtering
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_analysis_1.html

27. Filtering
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_analysis_1.html

28. Code for filtering
>>> for subject_id in range(1, 20):
>>> subject = "sub%03d" % subject_id
>>> for run in range(1, 7):
>>> raw_in = ...
>>> raw = mne.io.read_raw_fif(raw_in, preload=True, add_eeg_ref=False)
>>> raw.filter(1, 40, l_trans_bandwidth=0.5, h_trans_bandwidth='auto',
>>> filter_length='auto', phase='zero', fir_window='hann')
http://mne-tools.github.io/mne-biomag-group-demo/auto_scripts/02-python_filtering.html

29. Filter design
http://martinos.org/mne/stable/auto_tutorials/plot_background_filtering.html

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

31. Paradigm
Presentation Screen
Famous
Unfamiliar
Scrambled

32. Paradigm on trigger channel
>>> mask = 4096 + 256 # mask for excluding high order bits
>>> events = mne.find_events(raw, stim_channel='STI101',
>>> consecutive='increasing', mask=mask,
>>> mask_type='not_and', min_duration=0.003)
>>> fig = mne.viz.plot_events(events)
http://mne-tools.github.io/mne-biomag-group-demo/auto_scripts/03-run_extract_events.html

33. Paradigm in code
>>> events_id = {
>>> 'face/famous/first': 5,
>>> 'face/famous/immediate': 6,
>>> 'face/famous/long': 7,
>>> 'face/unfamiliar/first': 13,
>>> 'face/unfamiliar/immediate': 14,
>>> 'face/unfamiliar/long': 15,
>>> 'scrambled/first': 17,
>>> 'scrambled/immediate': 18,
>>> 'scrambled/long': 19,
>>> }
Use “tags” to
describe events

34. From Epochs to Evoked
>>> tmin, tmax = -0.2, 0.8
>>> reject = dict(grad=4000e-13, mag=4e-12, eog=180e-6)
>>> baseline = None
>>>
>>> epochs = mne.Epochs(raw, events, events_id, tmin, tmax,
proj=True, picks=picks, baseline=baseline,
preload=True, decim=2, reject=reject,
add_eeg_ref=False)
http://mne-tools.github.io/mne-biomag-group-demo/auto_scripts/05-make_epochs.html
…reject the ones with blinks

35. http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_analysis_1.html
why did we drop epochs?
Example of “drop log” for subject 1:

36. Use ICA on Epochs to remove ECG
>>> ica_name = op.join(meg_dir, subject, 'run_%02d-ica.fif' % run)
>>> ica = read_ica(ica_name)
>>> n_max_ecg = 3 # use max 3 components
>>> ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5)
>>> ecg_inds, scores_ecg = ica.find_bads_ecg(ecg_epochs,
method='ctps',
threshold=0.8)
>>> ica.exclude += ecg_inds[:n_max_ecg]
>>> ica.apply(epochs)
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_ica.html

37. Use ICA on Epochs to remove ECG
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_ica.html

38. Fit and apply ICA on Epochs
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_ica.html

39. From Epochs to Evoked
>>> evoked_faces = epochs['face'].average()
>>> evoked_famous = epochs['face/famous'].average()
>>> evoked_scrambled = epochs['scrambled'].average()
>>> evoked_unfamiliar = epochs[‘face/unfamiliar'].average()
>>> contrast = mne.combine_evoked([evoked_famous,
>>> evoked_unfamiliar,
>>> evoked_scrambled],
>>> weights=[0.5, 0.5, -1.])
>>> mne.evoked.write_evokeds('%s-ave.fif' % subject,
[evoked_famous, evoked_scrambled,
evoked_unfamiliar, contrast,
faces])
http://mne-tools.github.io/mne-biomag-group-demo/auto_scripts/06-make_evoked.html
Index Epochs withtags

40. EEG Evoked for 3 first subjects
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_compare.html

41. EEG Evoked topographies (6 subjects)
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_compare.html

42. MEG Evoked for 3 first subjects
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_compare.html

43. MEG Evoked for 3 first subjects
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_compare.html

44. Back to filtering…
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_fanning.html
>>> raw.filter(None, 40, fir_window='hann', phase='zero',
h_trans_bandwidth=‘auto’, filter_length='auto')

45. Back to filtering…
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_fanning.html
>>> raw.filter(None, 40, fir_window='hann', phase='zero',
h_trans_bandwidth=‘auto’, filter_length='auto')
Fanning
problem

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

49. Grand averaging
>>> for idx, evokeds in enumerate(all_evokeds):
>>> # Combine subjects:
>>> grand_avg[idx] = mne.combine_evoked(evokeds, 'equal')
>>> mapping = {'Famous': evokeds[0], 'Scrambled': evokeds[1],
'Unfamiliar': evokeds[2]}
>>> mne.viz.plot_compare_evokeds(mapping, [idx])
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_group.html

50. Grand averaging
>>> for idx, evokeds in enumerate(all_evokeds):
>>> # Combine subjects:
>>> grand_avg[idx] = mne.combine_evoked(evokeds, 'equal')
>>> mapping = {'Famous': evokeds[0], 'Scrambled': evokeds[1],
'Unfamiliar': evokeds[2]}
>>> mne.viz.plot_compare_evokeds(mapping, [idx])
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_group.html
From baseline to baseline !

51. MNE vs.Wakeman et al.
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_group.html

52. MNE vs.Wakeman et al.
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_group.html
Real?

53. Sensors stats (cluster level)
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_sensor_cluster_stats_eeg_channel.html

54. Sensors stats (cluster level)
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_sensor_spatio_temporal_cluster_stats.html
p = 0.01 (corrected)

55. Decoding
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_time_decoding.html
>>> td = TimeDecoding(predict_mode='cross-validation',
times=times, scorer='roc_auc')
>>> td.fit(epochs, y)
http://mne-tools.github.io/mne-biomag-group-demo/auto_scripts/08-run_time_decoding.html

56. Source space analysis
•3 Layers BEM forward modeling
•dSPM source localization on cortical surface
•loose orientation (0.2) & depth (0.8) & SNR = 3
•RMS across orientations
•morphing to fsaverage:
subject fsaveragemorphing
… the MNE (default) way

57. Source space analysis
http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_analysis_2.html
Subject 2
Contrast
localization
(faces vs.
scrambled

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

59. MNE web report
http://perso.telecom-paristech.fr/~mjas/biomag_demo/report_sub002.html

60. MNE web report
http://perso.telecom-paristech.fr/~mjas/biomag_demo/report_sub002.html
Command line:
$ mne report --path MEG/sub002 --
info MEG/sub002/run_01_filt_sss-
epo.fif --subject sub002 --subjects-
dir subjects/ --verbose --jobs 6

61. Getting help http://martinos.org/mne/
http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
Mailing list:

62. Getting inspired...
http://martinos.org/mne/auto_examples/index.html

63. Sending feedback
https://github.com/mne-tools/mne-python

64. Some links
• Documentation:
• http://martinos.org/mne/ (general doc)
• http://martinos.org/mne/stable/manual/index.html (manual)
• http://martinos.org/mne/stable/tutorials.html (tutorials with code)
• http://martinos.org/mne/auto_examples/index.html (python examples)
• Code:
• https://github.com/mne-tools/mne-python (mne-python code)
• https://github.com/mne-tools/mne-matlab (mne matlab toolbox)
• https://github.com/mne-tools/mne-scripts (mne shell scripts)

65. Access HCP-MEG data as MNE-Python data structures
https:/mne-tools.github.io/mne-hcp

66. Seamlessly plug HCP MEG data into the Python ecosystem
…/mne-hcp/auto_tutorials/index.html
Poster
[Mo-P008]
Engemann et al.

67. Alexandre Gramfort
http://alexandre.gramfort.netContact:
GitHub : @agramfort Twitter : @agramfort
If you want a
sticker come to the
MNE poster !
Open Science!