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| The best way to learn MNE-Python is to check out the intro and examples on the [[http://mne-tools.github.com/mne-python-intro/|MNE-Python intro]] site. | The best way to learn MNE-Python is to check out the intro and examples on the [[http://mne-tools.github.com/mne-python-intro/|MNE-Python intro]] and [[https://martinos.org/mne/stable/auto_examples/index.html|MNE-Python examples]] sites. |
Using MNE Python at the CBU
MNE is a software developed for EEG/MEG analysis at the Martinos Centre for Biomedical Imaging. It was originally developed for use under Linux (see e.g. MNE examples, and MNE at the CBU.
Recently, MNE has been integrated into Python for optimization and automatization, as described on the MNE-Python intro pages.
In order to use the latest MNE-Python version at the CBU, type
mne_python
in your Linux command window, which will run it in the iPython environment on one of the linux boxes 43-56. This might take a while to start - please be patient.
In order to use a specific version, type for example
mne_python -v 0.7
In order to check whether you've got access to the MNE Python tools, type
mne. <tab>
(where <tab> means pressing your tab key, not enter), and you'll see a list of MNE commands.
The best way to learn MNE-Python is to check out the intro and examples on the MNE-Python intro and MNE-Python examples sites.
They provide a sample data set, and a large number of example analysis scripts that can be directly applied to those data.
It's all quite new and exciting, so any comments or feedback would be welcome! Please contact OlafHauk.