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Lessons learned from processing the
Human Connectome MEG data
Denis A. Engemann
denis-alexander.engemann@inria.fr
www.denis-engemann.de
github: dengemann
Biomag 2018 Philadelphia
Q: Why would you care about a
Python reader for the HCP MEG data?
Bridging resources
• Dataset provider: make sure the data can be used
with any tool.
• Software provider: make sure the software can be
used with any dataset
• Researcher: use a baseline dataset in a most
unconstrained way. Enabling cross-toolbox, cross-
methods, cross-site comparisons
Example 1: validation of power envelope connectivity
Soon in
CTF Brainstorm
Hipp et al. Nat Neuro 2012
CAMCAN
HCP
Example 2: Benchmark against new MNE family solvers
Challenge me!
Example 3: compare machine learning tools
Challenges we solved together
• The ethics protocol may not allow sharing all
aspects of the data
• Derivative data may be stored differently from what
your tools expects (anatomy, coordinate systems)
• Knowing which information is which file and how to
get the missing bits and pieces
+ +
Programmatic access to HCP data
https:/mne-tools.github.io/mne-hcp
…/mne-hcp/auto_tutorials/index.html
Add your
example!
Run all analyses supported by MNE on HCP data
Add your
example!
Load the evoked responses
Add your
example!
Get the forward model and compute the noise covariance
Add your
example!
Compute the inverse solution
Add your
example!
Be fascinated by visual cortex activity
The lessons learned
• Principle of charity: there can be good reasons why
some data are not easily available.
• Work together with data providers
• Document how a data resource can be used with
your software
• Mind your community’s software eco-system
• Talk to each other!
Thank you
• Robert Oostenveld
• Jan-Mathijs Schoffelen
• Giorgos Michalareas
• Eric Larson
• Alexandre Gramfort
• Mainak Jas
• Virginie van Wassenhove

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BIOMAG2018 - Denis Engemann - MNE-HCP