[Eeglablist] Release of the BeMoBIL pipeline as EEGLAB plugin

[Marius Klug]

Dear list,

we're super excited to announce that after years of progress, our analysis
pipeline for import, synchronization, and processing of Mobile Brain/Body
Imaging (MoBI) data, the BeMoBIL Pipeline, is now released as a plugin to
EEGLAB! It is an open-source toolkit for fully synchronized, automatic,
transparent, and replicable import, processing and visualization of MoBI
and other EEG data. It includes wrappers for EEGLAB functions, uses various
EEGLAB plugins, and comes with additional new functionalities.

The main features are:

- Seamless integration of the lab streaming layer .xdf data format as well
as BIDS import including the BIDS-mobi extension.

- A robust preprocessing protocol including automatic noise removal, robust
bad channel detection and interpolation, and full rank average referencing.

- Independent Component Analysis with suitable prerequisites and including
automatic time-domain artifact removal, as well as fitting of EEG
equivalent dipoles and automatic classification of components and final
cleaning of the data.

- An easy-to-use central configuration file that contains all relevant
tunable parameters (and explanations). It is optimized for MoBI data, which
is typically contaminated with motion artifacts (more muscle movement,
cable sway, fluctuating line noise...), but should be also applicable to
stationary data with minor parameter tuning. Using the configuration
struct, users can also easily customize the pipeline to fit the needs of
their own data.

- Each processing step is documented within the EEG.etc struct and
analytics plots are generated throughout the process to enable quick and
effective checks of the data.

- Additionally, the pipeline includes preprocessing of motion data that is
synchronized to the EEG data at import. This way, users are able to analyze
the EEG and motion streams "together", meaning that events extracted from
motion data can be copied to the EEG data, for example.

- A functionality to improve the group-level analysis for sources (ICs) by
using a repeated clustering approach that optimizes the clustering for a
given region of interest.

A comprehensive guide to using and understanding the pipeline can be found
in our wiki on github: https://urldefense.com/v3/__https://github.com/BeMoBIL/bemobil-pipeline/wiki__;!!Mih3wA!Ao32GNU_MO-LOz_tS23Z_2ogfps_iu3BXDekxsOjRgBupCKRnOVQaZT_UkOnUIgQLx6vp4Q8IpkPc_yIEzH4WoeTaTp8$ 

This plugin is script-based only, so there is no GUI as of now, but the
scripts should be easy to use, at least that was the plan!

You can follow the quick start example as described here:

👉 https://urldefense.com/v3/__https://github.com/BeMoBIL/bemobil-pipeline__;!!Mih3wA!Ao32GNU_MO-LOz_tS23Z_2ogfps_iu3BXDekxsOjRgBupCKRnOVQaZT_UkOnUIgQLx6vp4Q8IpkPc_yIEzH4WntR41O9$ 
▶️ There's an example data repository as well as example scripts, and a
single "run_all_examples" script that--surprise--runs all examples! Be sure
to install all requirements as laid out in the installation guide on the
wiki

If you use the pipeline you can also cite the preprint here:

Klug, M., Jeung, S., Wunderlich, A., Gehrke, L., Protzak, J., Djebbara, Z.,
Argubi-Wollesen, A., Wollesen, B., & Gramann, K. (2022). The BeMoBIL
Pipeline for automated analyses of multimodal mobile brain and body imaging
data. In bioRxiv. https://urldefense.com/v3/__https://doi.org/10.1101/2022.09.29.510051__;!!Mih3wA!Ao32GNU_MO-LOz_tS23Z_2ogfps_iu3BXDekxsOjRgBupCKRnOVQaZT_UkOnUIgQLx6vp4Q8IpkPc_yIEzH4WtTnYXy-$ 

If you want to contribute, fantastic! Reach out to me by mail, make an
issue on the git repository, start a fork, send a pigeon, or say hello on
the BeMoBIL discord: https://urldefense.com/v3/__https://t.co/cj5yn8FWsl__;!!Mih3wA!Ao32GNU_MO-LOz_tS23Z_2ogfps_iu3BXDekxsOjRgBupCKRnOVQaZT_UkOnUIgQLx6vp4Q8IpkPc_yIEzH4WlFsJ0yt$ 

This is a massive group effort with contributions from Sein Jeung, Anna
Wunderlich, Lukas Gehrke, Janna Protzak, Zakaria Djebbara, Andreas
Argubi-Wollesen, Bettina Wollesen, & Klaus Gramann, and of course, all
other testers who gave invaluable feedback throughout the last years!

We hope this helps make research more reproducible and produce reliable
results more quickly!

Cheers,
Marius

-- 
Marius Klug
Research Associate at TU Berlin, Germany
Department of Biological Psychology and Neuroergonomics
+49 (0)30 314-79 528
Project Homepage
<https://urldefense.com/v3/__https://blogs.tu-berlin.de/bpn_bemobil/projects/neuroergonomics-of-situation-awareness-nesita/__;!!Mih3wA!Ao32GNU_MO-LOz_tS23Z_2ogfps_iu3BXDekxsOjRgBupCKRnOVQaZT_UkOnUIgQLx6vp4Q8IpkPc_yIEzH4WiVsv4WP$ >