Dear MNE-ers,
As you might be aware, I was one of the two participants working with
MNE-Python during the 2013 edition of Google Summer of Code (GSoC). It was
a unique and interesting experience, and I would like to share some of my
work with you all.
The aim of my project (as Alex previously mentioned) was to add
functionality for real-time decoding of MEG data. The project was divided
into two phases of roughly one and a half months duration each.
In the first phase (see related pull request here:
WIP: Realtime decoding by mainakjas · Pull Request #615 · mne-tools/mne-python · GitHub), we introduced two
new modules in MNE-Python -- one for decoding, and another for real-time
analysis. The decoding module latches on to scikit-learn, which is a
well-established package for machine-learning in Python. At the moment, we
have provided an interface to perform common pre-processing and feature
extraction steps in MEG data such as scaling, filtering, estimation of
Power Spectral Density, Common Spatial Pattern etc. These can be easily
combined with classifiers in scikit-learn to do advanced machine-learning
analysis on MEG data, all in real time.
In the second phase (see related pull request here:
https://github.com/mne-tools/mne-python/pull/692/files), I worked on
real-time feedback, which will allow adaptive experimental paradigms.
These new features come with example scripts to play around with, which is
also the best way to get started.
The project is also documented in the form of weekly blog posts here:
http://www.ml-py-meg.blogspot.fi/ which shows the evolution of ideas over
time. All of this wouldn't have been possible without the vibrant
community of MNE developers on github, who have been very helpful
throughout the project. If you have any questions, comments or feedback,
they are most welcome!
Best Regards,
Mainak
Hi MNE community,
I am pleased to announce that Google with his program Google
summer of code (GSOC) offered this year 2 slots to MNE-Python.Mainak Jas from Aalto in Helsinki will work on real time decoding of
MEG data leveraging on the work in mne-cpp [1]
that allows the live streaming of MEG data buffers.Roman Goj from the University of Stirling in Scotland will work on
improving our beamformer support especially adding time-frequency
support.I am looking forward to a productive summer for both of them and I am
happy to see the mne-python community getting bigger.Best,
Alex[1] GitHub - mne-tools/mne-cpp: MNE-CPP: A Framework for Electrophysiology
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