Mapping the parameter space in EEG/MEG analyses (mapMEEG)

• Mapping the parameter space in EEG/MEG analyses (mapMEEG)
  • Idea and history
  • Future steps to do
  • How to contribute
  • Related projects
  • Useful links and other resources
    • Published guidelines and pipelines
    • Reporting of specific parameters
  • Contributors

Idea and history

This project was initiated at the OHBM Hackathon 2020 (initial issue here), intially as a git-0/ no code project. It is a project with an educational goal.

The idea was to provide help for less experienced EEG/ MEG researchers to get started on pre-registration featuring MEG/ EEG by creating a pre-registration template in a bottom-up manner.

More specifically, we pursued the following steps:

• identify existing pre-registrations featuring EEG/ MEG
• classify those pre-registrations according to a few criterion using this Google form.
  • which creates an overview in this Google spreadsheet (read-only link).
  • for a list of pre-registrations still to be classified see this list (read-only link).
• systematically screen those pre-registrations for the following criteria:
  • What are steps that are regularly under-specified?
  • What are steps that are regularly forgotten?
  • What are steps for which researchers regularly deviate from their initially pre-registered pipeline?
  • What are good practices that facilitate readability and understanding in the user?
• come up with a simple framework for an EEG/ MEG pre-registration pipeline
• create an adaptive shiny app that allows users to fill in typical EEG/ MEG pipeline parameters and automatically creates human-readable text that can be copy-pasted into a pre-registration
  • See this repository
  • Current prototype hosted on https://johalgermissen.shinyapps.io/mapMEEG_proto/

Future steps to do

• Spreadsheet overview of existing pre-registrations:
  • Code more pre-registration from the queue.
  • rate pre-registrations for level of elaboration/ detail-
  • compare pre-registrations to finally published journal articles to identify deviations.
  • compare pre-registrations to registered reports (level of detail, deviations).
  • identify pre-processing steps specific to MEG.
• Shiny app:
  • Separate tabs for data collection, data pre-processing, data analyses.
  • Separate tabs for EEG and MEG.
  • Allow researchers to fill in a certain step multiple times and feature it at different time points within their pipeline
  • Allow researchers to determine the order in which steps are outputed in the text document
  • Helper links with:
    • short explanation on each parameter.
    • short example.
    • reference to respective software options in commonly used packages (e.g. EEGLAB, Fieldtrip, MNE Python)
    • reference to further literature

How to contribute

If you'd like to get involved, join the hbmhack-mapMEEG channel on the brainhack mattermost. Join our channel

Alternatively, you can open a new issue on this repository itself if there is something you would like to discuss directly here. We're also happy about direct pull requests to improve our app.

Related projects

• EEGManyLabs project (related tweets on Twitter)
• ManyPipelines project (launch tweet on Twitter)
• EEG Pre-registration template started at SIPS 2019 and continued at the MPI-CBS, Leipzig, Germany

Useful links and other resources

Published guidelines and pipelines

• COBIDAS-MEEG guidelines: https://cobidasmeeg.wordpress.com/
  • Preprint Best Practices in Data Analysis and Sharing in Neuroimaging using MEEG
• Pipeline by Steve Luck on erpinfo.org
• Makoto's pre-processing pipeline in EEGLAB (Swartz Center for Computational Neuroscience, UCSD)
• Special Issue in Frontiers in Neuroscience 2017: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software., including among others:
  • Frömer, R., Maier, M., & Abdel Rahman, R. (2018). Group-level EEG-processing pipeline for flexible single trial-based analyses including linear mixed models. Frontiers in Neuroscience, 12, 48.
  • Jas, M., Larson, E., Engemann, D. A., Leppäkangas, J., Taulu, S., Hämäläinen, M., & Gramfort, A. (2018). A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good practices. Frontiers in neuroscience, 12, 530.
• Keil, A., Debener, S., Gratton, G., Junghöfer, M., Kappenman, E. S., Luck, S. J., ... & Yee, C. M. (2014). Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. Psychophysiology, 51(1), 1-21.
• Robbins, K. A., Touryan, J., Mullen, T., Kothe, C., & Bigdely-Shamlo, N. (2020). How Sensitive are EEG Results to Preprocessing Methods: A Benchmarking Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(5), 1081-1090.

Reporting of specific parameters

• Beamforming: Jaiswal, A., Nenonen, J., Stenroos, M., Gramfort, A., Dalal, S. S., Westner, B. U., ... & Oostenveld, R. (2020). Comparison of beamformer implementations for MEG source localization. NeuroImage, 116797. (https://onlinelibrary.wiley.com/doi/full/10.1111/psyp.12639)
• Morlet wavelets: Cohen, M. X. (2019). A better way to define and describe Morlet wavelets for time-frequency analysis. NeuroImage, 199, 81-86.
• Temporal filtering: Luck, S. J., & Gaspelin, N. (2017). How to get statistically significant effects in any ERP experiment (and why you shouldn't). Psychophysiology, 54(1), 146-157.

Contributors

Thanks goes to these wonderful people (emoji key):

Johannes Algermissen 💻 🎨 🖋 🤔 🚇 🚧 📆 🔧 📋

dokato 💻 🎨 🖋 🤔 🔧

Martin Schulz 🖋 🤔

Yu-Fang Yang 🖋 🤔

Natalie 🖋 🤔

carolyog 🖋 🤔

This project follows the all-contributors specification. Contributions of any kind welcome!

LICENSE

MIT