GSOC Ideas

Denis A. Engemann edited this page Feb 1, 2018 · 80 revisions

Important: Expectations for prospective students

and from PSF: http://python-gsoc.org/

MNE-Python is planning to participate in the GSOC 2018 under the Python Software Foundation (PSF) umbrella. Here is our list of potential projects.


NOTE. If you are not currently pursuing research activities in MEG or EEG and do not use or do not plan to use MNE-Python for your own research our GSOC might not be for you. Our projects all require domain-specific interest and are not simple coding jobs.


0. Write a BIDS application for standard ERP/ERF analysis from sensor analysis to source space

Difficulty: medium (provided you know even MEG/EEG data analysis)

Possible Mentor: Denis Engemann, Alex Gramfort, Eric Larson, Mainak Jas

Goal: The aim of this project is to automate as much as possible processing of MEG datasets exposed in the newly created MEG-BIDS format.

Some MEG data available in BIDS format are available on OpenfMRI such as https://openfmri.org/dataset/ds000248/

1. Facilitate access to open EEG/MEG databases via the mne.datasets module

Difficulty: medium

Possible Mentor: Denis Engemann, Alex Gramfort, Eric Larson,

Goal: The aim of this project is to improve the access to open EEG/MEG databases via the mne.datasets module, in other works improve our dataset fetchers. There is physionet, but much more. Having a consistent API to access multiple data source would be great.

Subgoals:

See https://github.com/mne-tools/mne-python/issues/2852 and https://github.com/mne-tools/mne-python/issues/3585 for some ideas, or:

  • MMN dataset (http://www.fil.ion.ucl.ac.uk/spm/data/eeg_mmn/ ) used for tutorial/publications applying DCM for ERP analysis using SPM.
  • Human Connectome Project Datasets (http://www.humanconnectome.org/data/ ). Over a 3-year span (2012-2015), the Human Connectome Project (HCP) scanned 1,200 healthy adult subjects. The available data includes MR structural scans, behavioral data and (on a subset of the data) resting state and/or task MEG data.
  • Kymata Datasets (https://kymata-atlas.org/datasets). Current and archived EMEG measurement data, used to test hypotheses in the Kymata atlas. The participants are healthy human adults listening to the radio and/or watching films, and the data is comprised of (averaged) EEG and MEG sensor data and source current reconstructions.
  • http://www.brainsignals.de/ A website that lists a number of MEG datasets available for download.

2. Improve 3D visualization in the Jupyter notebook using ipyvolume

Difficulty: medium

Possible Mentor: Chris Holdgraf, Jean-Remi King

Goal: The aim of this project is to improve the visualization of 3D data in MNE when working in the Jupyter notebook.

Subgoals:

  • 2D: We currently use matplotlib for 2D visualization. However matplotlib's backend can be slow and difficult to interact with. In particular, it remains slow and/or difficult to make movies of moving topographies. One milestone would be to adapt the snapshots of plot_evoked_topomap to sliding movies. An example of such enhancement based on ipyvolume can be found here

  • 3D: We currently use Mayavi for 3D visualization, but the world of 3D visualization in the browser using WebGL is moving fast. The idea is to develop a working alternative to mayavi built on top of ipyvolume and ipywidgets. One approach would be to add an ipyvolume to the pysurfer package. An example of such enhancement can be found here

3. Improve non-parametric statistical functions in MNE

Possible Mentor: Mikolaj Magnuski, Alex Gramfort, Jean-Remi King

Related issue: https://github.com/mne-tools/mne-python/issues/4859