What is MEGqc?#
MEG data quality control#
Magnetoencephalography (MEG) data is susceptible to noise and artifacts, which can severely corrupt the data quality. These artifacts may arise from:
Environmental noise sources (e.g. powerline noise).
Internal noise sources (e.g. eye movements of the subject).
Systemic noise sources (e.g. malfunction of a sensor).
For this reason, quality control of MEG data is an essential step for ensuring valid and reproducible science (Niso et al., 2022). However, the detection and annotation of artifacts in MEG data is commonly performed manually (visual inspection), requires expertise and can be a tedious and time-consuming task. Also, as there’s not a standardized procedure, it’s vulnerable to biases.
MEGqc#
To address this issue, the ANCP lab developed MEGqc, a software tool for automated and standardized quality control of MEG recordings. By providing a standardized workflow, it helps minimize human bias and facilitates comparisosn between datasets. MEGqc evaluates the quality of raw data, but it is not an artifact removal tool.
The MEGqc pipeline can be used in two ways: via a command-line interface (CLI) or a graphical user interface (GUI). This documentation covers the installation and tutorial for both options.
The CLI is recommended for developers or advanced users.
The GUI is ideal for beginners or users who prefer a more visual interface.
Regardless, MEGqc is designed to be user-friendly. To run the analysis, the user just needs to:
Provide data for evaluation.
Set analysis parameters if desired (default parameters are available).
Run the analysis.
To ensure standardization of the pipeline, MEGqc software is tailored to the BIDS standards.
Metrics in MEGqc#
The different calculation modules within MEGqc are called metrics and they are used to evaluate specific types of artifacts or aspects of data quality. MEGqc provides you with machine-readable outputs (JSON files and TSV files), and, to ensure clarity, MEGqc generates detailed visual HTML reports for each Metric. There are six independent metrics, and this documentation we will cover each of their HTML reports:
There are 2 other metrics within MEGqc
Peak-to-Peak automatic calculation: This module, which relies on MNE library functions, is not used in the final version of the pipeline. Instead, the manual “Peak-to-Peak manual” is recommended.
Head movement calculation: This module estimates subject’s head movements. These movements may appear as sudden shifts or jumps in the MEG data, causing distortions in the spatial distribution of the recorded magnetic fields. The effects of head movements can vary depending on the strength and direction. This module is implemented but requires a substantial amount of information to perform the calculation.
Next section#
In the next section, we’ll walk through the content of the HTML reports. For a deeper understanding of MEGqc’s core functionality, dependencies and derivatives, visit the pipeline basics page.