[Mne_analysis] [ANN] MNE-Python 0.10 Release

[Chris Holdgraf]

wooo! congrats! +100



On Tue, Oct 27, 2015 at 2:24 PM, Alexandre Gramfort <
alexandre.gramfort at telecom-paristech.fr> wrote:

> Hi,
>
> We are pleased to announce the new 0.10 release of MNE-python.
>
> A few highlights:
>
> Forward modeling:
> - Add support for BEM model creation with mne.make_bem_model
> - Add support for BEM solution computation mne.make_bem_solution
> - New "mne flash_bem" command to compute BEM surfaces from Flash MRI images
> - Add source space morphing
>
> Stats / Processing:
> - Add support for generalized M-way repeated measures ANOVA for fully
> balanced designs
> - Add new object mne.decoding.TimeDecoding for decoding sensors'
> evoked response across time
> - Add new method mne.preprocessing.Xdawn for denoising and decoding of
> ERP/ERF
> - Add new object mne.decoding.LinearModel for decoding M/EEG data and
> interpreting coefficients of linear models with patterns attribute
> - Adapt corrmap function (Viola et al. 2009) to semi-automatically
> detect similar independent components across data sets
>
> Visualisation:
> - Add interactive plotting of topomap from time-frequency
> representation by selection ROI in time-frequency plane
> - New ICA plotters for raw and epoch components
> - New epochs browser to interactively view and manipulate epochs
> - Add support for plotting patterns/filters mne.decoding.csp.CSP and
> mne.decoding.base.LinearModel
> - Add interactive plotting of single trials by right clicking on
> channel name in epochs browser
> - Add support for drawing topomaps by selecting a time-interval in
> mne.Evoked.plot
>
> Code quality:
> - Speed up TF-MxNE inverse solver with block coordinate descent
> - Speed up zero-phase overlap-add (default) filtering by a factor of
> up to 2 using linearity
> - Add support for saving large epochs into multiple files
> - Add support for jointly resampling a raw object and event matrix to
> avoid issues with resampling status channels
> - Add preload argument to mne.read_epochs to enable on-demand reads from
> disk
> - Big rewrite of simulation module. Allows to simulate raw with
> artefacts (ECG, EOG) and evoked data, exploiting the forward solution.
>
> Dataset / reproducible results
> - Add fetcher mne.datasets.brainstorm for datasets used by Brainstorm
> in their tutorials
>
> Documentation:
> - New web site with new documentation structure to facilitate
> documentation improvements.
>
> For a full list of improvements and API changes, see:
>
> http://martinos.org/mne/stable/whats_new.html#version-0-10
>
> To install the latest release the following command should do the job:
>
> pip install --upgrade --user mne
>
> As usual we welcome your bug reports, feature requests, critics and
> contributions.
>
> Some links:
>
> - https://github.com/mne-tools/mne-python (code + readme on how to
> install)
> - http://martinos.org/mne (full MNE documentation)
>
> Follow us on Twitter: https://twitter.com/mne_python
>
> Regards,
> The MNE-Python developers
>
> People who contributed to this release with their number of commits:
>
> The committer list for this release is the following (preceded by
> number of commits):
>
>    273  Eric Larson
>    270  Jaakko Leppakangas
>    194  Alexandre Gramfort
>    128  Denis A. Engemann
>    114  Jona Sassenhagen
>    107  Mark Wronkiewicz
>     97  Teon Brooks
>     81  Lorenzo De Santis
>     55  Yousra Bekhti
>     54  Jean-Remi King
>     48  Romain Trachel
>     45  Mainak Jas
>     40  Alexandre Barachant
>     32  Marijn van Vliet
>     26  Jair Montoya
>     22  Chris Holdgraf
>     16  Christopher J. Bailey
>      7  Christian Brodbeck
>      5  Natalie Klein
>      5  Fede Raimondo
>      5  Alan Leggitt
>      5  Roan LaPlante
>      5  Ross Maddox
>      4  Dan G. Wakeman
>      3  Daniel McCloy
>      3  Daniel Strohmeier
>      1  Jussi Nurminen
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-- 
_____________________________________

PhD Candidate in Neuroscience | UC Berkeley <http://hwni.org/>
Editor and Web Director | Berkeley Science Review
<http://sciencereview.berkeley.edu/>
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