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Updated: Jan 25, 2026

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
Published on: June 6, 2025
Brainstorm Pipeline Analysis of Resting-State Data From the Open MEG Archive
Guiomar Niso1,2,3,4, Francois Tadel1,5,6, Elizabeth Bock1
1McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
This study introduces a straightforward analysis pipeline for resting-state magnetoencephalography (MEG) data. The Brainstorm software enables reproducible group analysis of brain activity in standard frequency bands.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Biophysics
Background:
- Resting-state magnetoencephalography (MEG) provides valuable insights into intrinsic brain function.
- Standardized and reproducible analysis pipelines are crucial for large-scale neuroimaging studies.
- The Open MEG Archive (OMEGA) offers a valuable resource for collaborative research.
Purpose of the Study:
- To present a simple, reproducible analysis pipeline for resting-state MEG data.
- To demonstrate the utility of the Brainstorm software for MEG data analysis.
- To showcase the integration of the Brain Imaging Data Structure (BIDS) extension for MEG.
Main Methods:
- Applied a reproducible analysis pipeline using Brainstorm software.
- Utilized resting-state MEG data from the Open MEG Archive (OMEGA).
- Implemented group-level analysis of ongoing brain activity across standard electrophysiological frequency bands.
Main Results:
- Generated group maps of ongoing brain activity in typical frequency bands.
- Successfully applied the pipeline to OMEGA resting-state MEG data.
- Demonstrated the pipeline's capability for streamlined electrophysiological data analysis.
Conclusions:
- The presented pipeline offers a simple and reproducible method for analyzing resting-state MEG data.
- Brainstorm software facilitates efficient group analysis of neurophysiological data.
- The pipeline's compatibility with BIDS-MEG highlights its scalability for larger datasets.
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