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Introduction

The Pipeline Reference is a catalog of the core methods available in AutoCleanEEG.
Each method corresponds to a preprocessing step that can be called inside a Task class, from raw data import to ICA and quality control. These methods are designed to be:
  • Composable – each method takes an EEG object (Raw, Epochs, Evoked) and returns a transformed version for the next step.
  • Configurable – parameters can be set in the task’s config dictionary.
  • Transparent – every method is logged, and many produce annotated outputs or QC figures.

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Adding New Methods

AutoCleanEEG Pipeline is built to be extensible. You can add new functionality in two main ways:
  1. Extend the Task class
    • Write a custom method inside your Task file.
    • Call it from run() at the point you want it executed.
    • Return a valid MNE object so downstream steps can continue.
  2. Contribute to the pipeline core
    • Add a new method to autoclean.core.task.Task.
    • Document it in the Pipeline Reference with its parameters and outputs.
    • This makes it available to all Task files.
Because the system passes full MNE objects through each step, new methods can leverage:
  • The entire MNE-Python API (filtering, time-frequency analysis, source localization).
  • External libraries (scikit-learn, NumPy, SciPy).
  • Even MATLAB or EEGLAB functions, by exporting/importing intermediate files or using the MATLAB Engine for Python.

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Our Approach During Development

📖 Documentation in Progress
We are actively expanding the Pipeline Reference. Some methods are fully documented here, while others are listed as part of our default task templates during development.This ensures users can see the full sequence of steps used by default, even if individual method pages are still being written.
Default templates include steps such as:
  • Import and resample
  • Filtering (high-pass, low-pass, notch)
  • Bad channel detection
  • Re-referencing
  • ICA and component classification
  • Epoching and epoch cleaning
  • QC reporting

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Big Picture

Think of the Pipeline Reference as both a library of reusable functions and a map of the default workflow. You can stick to the defaults, or extend the pipeline with your own methods, branching logic, and external tools. The goal is to make preprocessing transparent, reproducible, and adaptable to the needs of your study.