pyAutomagic for automatic EEG preprocessing and quality assessment

What does this implement/fix?
There is a need for automatic and replicable preprocessing and evaluation of data quality in EEG to increase transparency in the field. A package for this purpose, Automagic, was developed in Matlab (https://github.com/methlabUZH/automagic) by @nickilanger. It provides the user an easy way to set preprocessing and quality evaluation parameters for an entire project, and automatically execute. The quality metrics used are all based on variance of some type in the data, and these metrics are used to give each recording a rating of "good", "ok", or "bad". All results, figures, and log files, are then saved.

Proposed solution
The suggested solution, pyAutomagic, is the movement of Automagic's functionality to python, specifically compatible with mne-python and mne-bids. The outcome would be a repository in mne-tools.
In addition, a solution would include:

• some documentation for reference on what functions do, and meta-summary of the pipeline
• some tutorials on how to run full pipeline, sub-parts of the pipeline, and then example analysis at the end
• design change(s) summary compared to Automagic.
  The package would be tested to the same validation dataset used for the original Automagic package (https://osf.io/5e74x/files/).

Additional information
More info on the original (MATLAB) package functionality can be found in the paper published (https://www.biorxiv.org/content/10.1101/460469v3.full).

@adam2392

Review the original Automagic repository, its paper, and the OSF validation dataset to map the preprocessing and quality-assessment functionality proposed for pyAutomagic. Done would require a Python package compatible with mne-python and mne-bids, reference documentation, tutorials, a design-change summary, and validation against the original dataset.