Pipeline basics

Pipeline basics#

This section summarizes the MEGqc execution model and dependency roles.

General pipeline structure#

Pipeline

At high level:

  1. Read runtime configuration (settings.ini) and execution flags.

  2. Discover BIDS-compatible MEG data with ANCPBIDS.

  3. Apply minimal preprocessing (crop/filter/resample/epoching).

  4. Compute selected metric modules.

  5. Write machine-readable derivatives (TSV, JSON).

  6. Generate report layers from derivatives (subject/group/multisample; QA/QC).

Report levels#

  • QA Subject: per-subject interactive reports.

  • QA Group: dataset-level QA aggregation.

  • QA Multisample: cross-dataset QA comparison.

  • QC Group: GQI-based QC summary per dataset.

  • QC Multisample: cross-dataset QC comparison.

Derivative formats#

MEGqc uses machine-readable derivatives aligned with BIDS conventions:

  • JSON: structured summaries and metadata.

  • TSV: tabular metric-level details used by plotting and downstream processing.

Dependencies (what each one is used for)#

Core/direct dependencies in current package:

  • ancpbids: BIDS dataset traversal and derivatives handling.

  • mne: MEG signal I/O and domain-specific signal processing.

  • pandas: tabular computations and derivative tables.

  • plotly: interactive report figures.

  • joblib: subject-level parallelism.

  • pyqt6: GUI framework.

  • pyqt6-plugins (non-macOS): GUI platform plugin support.

  • numba: acceleration for selected kernels (for example manual PtP paths).

  • psutil: runtime system information in GUI (CPU/RAM awareness).

  • matplotlib: auxiliary plotting utilities in selected report modules.

  • prompt_toolkit: terminal UX utilities used by command-line workflows.

Additional libraries may appear as transitive dependencies via core packages.

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