meg-qc 0.7.9

Tool for automated MEG data quality control

MEGqc

MEGqc is an open-source, BIDS-aligned toolbox for automated MEG quality assessment (QA) and explicit quality control (QC) summarization.

It is designed for large cohorts and reproducible workflows, and provides both:

• interactive HTML reports for human inspection, and
• machine-readable derivatives for downstream automation.

What MEGqc Provides

• QA-first quality profiling of raw MEG signal quality before exclusion decisions.
• Multi-metric coverage including:
  • standard deviation (STD),
  • peak-to-peak amplitude (PtP),
  • power spectral density (PSD),
  • ECG/EOG-related contamination,
  • high-frequency muscle burden,
  • optional head-motion summaries.
• Multi-scale reporting across recording, channel, epoch, subject, dataset group, and multi-sample comparisons.
• QC support layer with configurable module-level criteria and a Global Quality Index (GQI).
• Reproducible execution with profile-aware outputs and saved settings provenance.
• Three usage modes: CLI, GUI, and programmatic dispatchers.

Requirements

• Python 3.10
• MEG data organized according to BIDS/MEG-BIDS.

Installation

Option 1: Installer-based (recommended for most users)

Download the installer bundle from the MEGqc releases and follow the platform-specific instructions in the installation guide.

Option 2: CLI-based (Conda + pip)

conda create -n megqc-py310 python=3.10 pip -y
conda activate megqc-py310
pip install meg-qc

For detailed installation instructions, see the CLI installation guide.

Quick Start (CLI)

1. Export default config:

get-megqc-config --target_directory ./config

2. Run QA/QC calculation:

run-megqc --inputdata /path/to/bids_dataset --config ./config/settings.ini

3. Build plotting reports:

run-megqc-plotting --inputdata /path/to/bids_dataset

4. Recompute GQI summaries (optional):

globalqualityindex --inputdata /path/to/bids_dataset

5. Run full pipeline in one command (calculation + plotting):

run-megqc --inputdata /path/to/bids_dataset --config ./config/settings.ini --run-all

Launch GUI

megqc

The GUI uses the same backend logic as CLI dispatchers and writes the same derivative/report outputs.

HPC and CBRAIN Deployment (Apptainer)

MEGqc can be containerized with Apptainer and deployed on any Linux HPC system, including CBRAIN — the distributed computing platform at the Montreal Neurological Institute (MNI) / McGill University, which MEGqc is being integrated into as part of an active collaboration.

The hpc/ folder contains the complete deployment materials:

File	Description
hpc/MEGQC.def	Apptainer definition file (source of truth for building the image)
hpc/BUILD_APPTAINER_IMAGE.md	Build guide: prerequisites, image creation, GUI launch, distribution
hpc/CLI_REFERENCE.md	Full CLI reference + CBRAIN/Slurm job script template

Quick start:

# Build the image
apptainer build MEGqc.sif hpc/MEGQC.def

# Run full analysis on a BIDS dataset
apptainer run --containall \
  --bind /path/to/bids_dataset:/mnt_IN \
  --bind "$PWD/settings.ini":/mnt_config/settings.ini \
  --bind "$PWD/outputs":/mnt_OUT \
  MEGqc.sif \
  --inputdata /mnt_IN --config /mnt_config/settings.ini \
  --derivatives_output /mnt_OUT --analysis_mode new --run-all --all

See hpc/README.md for the full overview and attribution.

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Typical Outputs

MEGqc writes outputs under BIDS derivatives (default):

• derivatives/Meg_QC/calculation/ — metric tables + JSON summaries
• derivatives/Meg_QC/reports/ — interactive HTML reports
• derivatives/Meg_QC/summary_reports/ — QC summaries including GQI artifacts

Documentation

• Installation (Installer-based): https://ancplaboldenburg.github.io/megqc_documentation/installation/gui.html
• Installation (CLI-based): https://ancplaboldenburg.github.io/megqc_documentation/installation/cli.html
• Tutorial: https://ancplaboldenburg.github.io/megqc_documentation/book/tutorial.html
• HTML Reports guide: https://ancplaboldenburg.github.io/megqc_documentation/book/report.html
• Full documentation: https://ancplaboldenburg.github.io/megqc_documentation/

Source Code

https://github.com/ANCPLabOldenburg/MEGqc

License

MIT License.