Dear MEG community,

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