MNE-Python 0.22 released

📢 Announcements
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Hello everyone,

we’re ahead of our typical release cycle and just published MNE-Python 0.22! :tada::gift::santa:

Please find a detailed list of changes and contributors below.

With this year coming to a close, we’d like to take this opportunity to thank you all for your continued support, and wish you and your loved ones Happy Holidays.

Stay healthy and take care! :mask:

All the best,

Your MNE Team.

A few highlights

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  • The 3D viewer of source time courses based on pyvista can now support picking labels from any freesurfer annotation. We highly recommend you now use pyvista and not pysurfer/mayavi for STC visualization.
  • Performing ICA is now much simpler for most users: instead of offering 3 parameters – n_components, n_pca_components, and max_pca_components – that would interact in often hard-to-understand ways, you can now simply pass a single parameter – n_components – to mne.preprocessing.ICA and get what you want. The n_pca_compoents and max_pca_components parameters have been deprecated and will be removed in MNE-Python 0.23. Please also see the “Notable API changes” section for details.
  • When plotting ICA sources via .ICA.plot_sources(), right-clicking on a component name will open a properties plot (the one you previously had to create using ICA.plot_properties()). This makes exploration of ICA data more interactive.
  • Annotations can now be shown and hidden interactively in raw plots using a checkbox. Extremely useful for datasets with overlapping Annotations!
  • Source estimates can now be baseline-corrected using their new apply_baseline() method.
  • The new function mne.stc_near_sensors() visualizes sEEG and ECoG data.
  • Fiducials can now be estimated when visualizing the coregistration by passing mri_fiducials=’estimated’ to mne.viz.plot_alignment().
  • Numerous improvements of volumetric source space support.
  • When cropping the baseline period of baseline-corrected Epochs, the information about the original baseline will be preserved to retain provenance.
  • We now offer spatio-spectral decomposition (SSD) via mne.decoding.SSD.
  • New readers: mne.read_evokeds_mff() for averaged MFFs, and mne.io.read_raw_boxy() for optical imaging data recorded using ISS Imgagent I/II hardware and BOXY recording software.

Notable API changes

We have changed a few things that will require you to adjust your code.

  • The n_pca_components and max_pca_components argument of mne.preprocessing.ICA has been deprecated, use n_components during initialization, and n_pca_components in ICA.apply() instead.
  • The trans argument of mne.extract_label_time_course() is deprecated and will be removed in 0.23 as it is no longer necessary.
  • The parameter event_colors in mne.viz.plot_epochs and mne.Epochs.plot() is deprecated, replaced by event_color which is consistent with mne.viz.plot_raw and provides greater flexibility.

Full list of API changes

Full changelog

For a full list of improvements and API changes, see https://mne.tools/stable/whats_new.html#version-0-22-0

Find the full documentation at https://mne.tools/stable/index.html

Installing the new release

Since quite a few things – including dependencies – have changed, we recommend creating a new environment with a “fresh” installation. Please follow the installation instructions on our website:

Feedback

As usual, we welcome your bug reports, feature requests, critiques, and contributions. Development takes place on GitHub. If you would like to contribute, star :star: the project, or just take a peek at the code, visit https://github.com/mne-tools/mne-python.

You may follow us on Twitter: https://twitter.com/mne_news

We hope you will enjoy the new features and many, many small improvements we have added, and are looking forward to receiving your feedback.

Stay safe and take care!

The MNE-Python developers

Contributors

MNE-Python is a community-driven project. We are always very happy to welcome new contributors of code and documentation! 34 people contributed to this release – and a whopping 10 were first-timers! Thank you all so very much for your time and effort, we truly appreciate it!

First-time contributors

  • Aniket Pradhan
  • Austin Hurst
  • Eduard Ort
  • Evan Hathaway
  • Hongjiang Ye
  • Jeff Stout
  • Jonathan Kuziek
  • Quianliang Li
  • Tod Flak
  • Victoria Peterson

Recurring contributors

  • Adam Li
  • Alexandre Gramfort
  • Christian Brodbeck
  • Clemens Brunner
  • Daniel McCloy
  • Denis A. Engemann
  • Eric Larson
  • Evgenii Kalenkovich
  • Fede Raimondo
  • Guillaume Favelier
  • Jean-Remi King
  • Jussi Nurminen
  • Keith Doelling
  • Kyle Mathewson
  • Mads Jensen
  • Mainak Jas
  • Marijn van Vliet
  • Mikolaj Magnuski
  • Olaf Hauk
  • Quianliang Li
  • Richard Höchenberger
  • Robert Luke
  • Stefan Appelhoff
  • Thomas Hartmann
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