-
-
Notifications
You must be signed in to change notification settings - Fork 1.4k
GSoC Ideas
MNE-Python is planning to participate in the GSOC 2023 under the 🐍 Python Software Foundation (PSF) umbrella.
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 require domain-specific interest and are not simple coding jobs.
MNE-Python is a pure Python package for preprocessing and analysis of M/EEG data. For more information, see our homepage.
For modes of communication see our getting help page. It's a good idea to introduce yourself on our Discourse forum to get in touch with potential mentors before submitting an application.
⚠️ "MNE-Python" must be in the title of your application for us to consider it!⚠️ - Student application information for Python
We list some potential project ideas below, but we welcome other ideas that could fit within the scope of the project!
Medium
350 hours
mne-qt-browser is a modern eletrophysiology browser based on Qt. It offers fast visualization of raw, epochs, and ICA time courses. However, there are many UI and usability improvements that could be added.
- Improve the overview bar
- Enable interactive switching between time courses and STFT/spectrogram view for individual traces, including UI elements to control various parameters (clim, cmap, n_fft, etc.)
- Add a two-control "time slider" and/or "channel slider" that allows setting the time span and channel span to show
- Optionally, add text overlays for each channel giving their value at the current time point (continuously updating)
- Add the possibility to load a file from the UI (add button or menu) or to simple drop a file in the window to view it
- Multiple other ideas on this project page
Medium
350 hours
We have an excellent 3D viewer for brain activations, and multiple ways of viewing evoked data (plot_topo, plot_topomap, plot_joint, etc.). We want to integrate these so that you can, for example, click on a time point in a Brain and have the evoked.plot_topomap update. This should be done using callbacks to allow for customizability.
- Design and implement a callback system for Brain and time-based viewers (plot_topo, plot_topomap, plot_joint, etc.)
- Provide a simple API for hooking
- Possibly allow passing an
(evoked, inv)pair to Brain to generate source time courses on the fly - Possibly allow integrating the
evoked.plot_*matplotlib figures into the brain viewer directly (this is a UI design problem mostly: should it use tabs, or something else?)
Medium
175 or 350 hours
Alex Gramfort, Eric Larson, Dan McCloy
MNE-Python has a PR open to add Eyelink data reading and channel types. After this is merged (possibly with GSoC help!), standard eye-tracking data preprocessing, regression, and visualization methods will be needed. This requires some domain-specific knowledge, i.e., some experience with eye-tracking data collection and analysis.
Any of the following would be helpful:
- Finish data reading PR
- Add any necessary preprocessing functions (interpolation during blinks, etc.)
- Extend support and add an example of using linear regression / deconvolution with a suitable pupil kernel and/or kernel estimation
- Visualization functions for time-varying network directed/undirected connectivity
- Adapt any other necessary code from the obsolete pyeparse
175 or 350 hours
Medium (requires knowledge of M/EEG data)
Denis Engemann, Alex Gramfort, Eric Larson, Daniel McCloy
The aim of this project is to improve the access to open EEG/MEG databases via the mne.datasets module, in other words, improve our dataset fetchers. There is physionet, but much more. Having a consistent API to access multiple data source would be great.
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.
- BNCI Horizon (http://bnci-horizon-2020.eu/database/data-sets) has several BCI datasets