ffrprep: A BIDS-App for preprocessing and analysing FFR EEG data.

Introduction

ffrprep is a BIDS-App that preprocesses frequency-following response (FFR) EEG data and emits per-trial-type, combined, and difference evoked responses alongside FFR-specific scalar metrics and HTML reports.

This documentation showcases the respective functionality and provides details concerning its application and modules. If you still have questions after going through what’s provided here you can refer to the Reference API or ask a question on GitHub.

Quickstart

Process two subjects (both preprocessing and analysis stages, four parallel workers per subject) with the published Docker image:

docker run --rm \
  -v /path/to/your/bids_dataset:/data:rw \
  ksitek/ffrprep:latest \
  /data /data/derivatives participant \
  --participant_label 01 02 \
  --stage both \
  --n_procs 4

Outputs land under /path/to/your/bids_dataset/derivatives/ffrprep-preprocessing/ and …/ffrprep-analysis/, each carrying a sub-XX_<stage>_report.html per subject. See Installation for image pinning and a Singularity build, and Usage for the full list of CLI flags plus worked examples (per-trial-type analysis, explicit difference pairs, cluster invocations).

🛠️ Installation

Set up ffrprep via Docker, Singularity, or a local Python install with uv.

Installation

🚀 Usage

Command-line reference and ready-to-copy example invocations for typical FFR preprocessing + analysis runs.

Usage

📘 Tutorial walkthrough

End-to-end walkthrough: download the example dataset, run the pipeline, and inspect the generated reports.

Tutorial walkthrough

🧠 Pipeline details

Per-stage breakdown of the BIDS validation, preprocessing, and analysis workflows, including the file layout each stage produces.

Pipeline details

Support & Contributing

Bug reports, feature requests, and pull requests go through the GitHub issue tracker. Usage questions are best asked on NeuroStars under the ffrprep tag, where the broader BIDS / neuroinformatics community sees them and prior answers are searchable. If you build something on top of ffrprep (custom downstream analyses, additional report sections, extra QC metrics), the section builders in ffrprep.reports accept extra_summary and extra_figures kwargs so you can fold caller-computed scalars or figures into the existing per-section tables and galleries without forking the pipeline.

🐛 Report issues

Bug reports, feature requests, and PRs welcome.

Open an issue on GitHub

💬 Get help

Usage questions get the broadest audience on NeuroStars under the ffrprep tag.

Ask on NeuroStars

📚 API reference

Public functions of ffrprep.preproc, ffrprep.analysis, ffrprep.reports, ffrprep.datasets, and ffrprep.utils.

Module-by-module reference

Reference API

🔄 Changelog

Per-version list of CLI flags, file-layout changes, and new helpers.

Release history

Release History