MEEGqc
Automated quality assessment and quality control for MEG and EEG data.
MEEGqc is an open-source, BIDS-aligned Python toolbox for reproducible quality assessment (QA) and quality control (QC) of MEG and EEG data. It generates machine-readable quality descriptors and interactive reports across subject-level, dataset-level, and multi-dataset-level scopes, supporting both detailed inspection and large-scale dataset quality screening.
Data quality is the bottleneck.
MEG and EEG signals are highly sensitive to physiological, environmental, hardware-related, and motion-related noise. In the absence of standardized quality procedures, researchers often assess data quality manually and subjectively, using group- or study-specific criteria that are rarely documented in a reusable way. This limits consistency, comparability, and reproducibility across datasets.
Manual review does not scale.
Visual inspection of hundreds or thousands of recordings is slow, operator-dependent, and hard to reproduce. Decisions drift across human reviewers, across recording sessions, and across acquisition sites.
QA and QC are conflated.
Most tools collapse the measurement of quality (QA) and the decisions made on top of it (QC) into one undocumented process. That makes it impossible to tell what was observed from what was decided.
Quality stays trapped in pipelines.
When quality information lives inside preprocessing outputs, it cannot be shared, queried, or reused independently. Cross-study comparability and FAIR data re-use suffer.
QA first, QC on top, both reproducible.
MEEGqc is an open-source, BIDS-aligned Python toolbox that puts quality assessment at the front of the workflow, then layers explicit, auditable quality-control decisions on top. One tool, three usage modes, three reporting levels.
Automated, standardized QA.
Seven complementary metrics quantify signal variability, power spectral density (PSD), physiological contamination (cardiac, ocular), muscle noise, and head motion. Outputs are persistent, machine-readable BIDS derivatives, not screenshots in a report folder.
Explicit QC with the Global Quality Index.
On top of the same derivative substrate, MEEGqc computes a single, configurable 0-100 GQI score per recording with a transparent penalty decomposition. Re-run with new thresholds; previous attempts are preserved with their exact parameters.
Built for MEG and EEG together.
Magnetometers, gradiometers, and EEG electrodes are first-class citizens. MEEGqc auto-detects the modality at file load and routes each recording through the right pipeline; EEG-only datasets and MEG recordings with embedded EEG channels both work.
Three reporting levels.
Subject-level, dataset-level, and multi-dataset-level reports cover every scope you need, from individual artifact triage to cross-dataset harmonization assessment.
Reproducible analysis profiles.
Every analysis pass is tagged with an
analysis_id and a frozen settings snapshot.
Multiple parameter variants can coexist for the same
dataset without overwriting each other.
GUI, CLI, or Python API.
The same backend dispatchers power all three modes. Run a click workflow on your laptop, scale to large datasets from a Slurm queue, or wire MEEGqc into a notebook for programmatic exploration.
Three reporting levels.
MEEGqc operates at subject-level, dataset-level, and multi-dataset-level - each available in both QA and QC flavours. Every report is a self-contained, interactive HTML page that travels with the data.
Subject-level per recording
The full quality profile of one subject across runs and tasks. Sensor maps, channel-by-epoch heatmaps, PSDs, ECG / EOG correlation views, and the subject's GQI summary.
Dataset-level per dataset
QA Group shows dataset distributions across
subjects, subject ranking, task-wise breakdowns, pooled
topomaps, and metric detail panels. QC Group
is the GQI-centric companion with score distributions and
penalty decomposition.
Multi-dataset-level across datasets
QA Multi-dataset compares raw signal
profiles; QC Multi-dataset compares GQI
scores. Built for cross-dataset harmonization,
longitudinal collection waves, and reference benchmarking.
Install in one click.
Double-click installers for Windows, Linux, and macOS set up
a portable Python environment and register MEEGqc as a
native app on your system. CLI users get a clean
pip install meg-qc path on Python 3.10 to 3.14.