EEGPrep

EEGLAB workflows, modern Python tooling

EEGPrep is a modernized Python port of core EEGLAB preprocessing workflows for EEG researchers. It preserves familiar concepts such as EEG, ALLEEG, pop_* commands, EEGBrowser review, ICA/component workflows, STUDY analyses, and command history, while making those workflows easier to run, review, automate, and share from Python.

Use this manual to move from a first dataset to a reproducible workflow. Start with the sample data, try the same steps in the GUI and eegprep-console, then turn the recorded pop_* history into scripts, pipelines, or agent-assisted analyses.

Start and Load Data

Install EEGPrep, launch the GUI/console, load eeglab_data.set, inspect the EEG structure, and save the result.

Quick Start

Learn the Data Model

Understand EEG, ALLEEG, events, epochs, channel locations, ICA fields, STUDY, and history replay.

Concepts Guide

Work in GUI and Console

Keep the main window and eegprep-console side by side: run menu actions, inspect EEG and history, then continue from Python.

GUI and Console Together

Follow GUI Workflows

Run menu workflows for filtering, rejection, ICA, ICLabel, EEGBrowser, STUDY, and DIPFIT while tracking the same session from the console.

GUI Tutorials

Script the Same Steps

Convert menu history into reusable Python scripts and batch workflows.

Scripting Workflows

Work With Research Agents

Use the EEGPrep CLI, JSON outputs, manifests, pipeline plans, and bundled agent skill guidance for AI-assisted EEG research projects.

Agent-Friendly EEGPrep CLI

Manual

Workflows

• User Guide
  • Getting Started
  • GUI, Console, and Scripts
  • Preprocessing and Review
  • Group and Plugin Workflows
  • Admin, Help, and Development
  • Common Entry Points
  • Help Coverage
• Examples

Reference

• Functions
  • Core and Session
  • GUI and Console
  • Data Loading and Saving
  • BIDS
  • Preprocessing and Artifacts
  • Epoching and Selection
  • ICA and Components
  • Filtering and Signal Processing
  • Time-Frequency
  • Plotting and Topography
  • Statistics
  • STUDY Workflows
  • Interactive Pop Workflows
  • Extension SDK and Registry
  • Minimal extension entry point
  • Status model
  • Catalog and Governance
  • Author Test Harness
  • Extension Manager and catalog
  • Authoring examples
  • API Reference
• FAQ
  • Installation FAQ
  • Usage FAQ
  • Performance FAQ
  • Troubleshooting FAQ
  • Comparison FAQ
  • Data Format FAQ
  • Getting Help
• References

Project

• Contributing to EEGPrep
  • Reporting Bugs
  • Getting Started
  • Development Environment
  • Code Style Guidelines
  • Testing Requirements
  • Documentation Standards
  • Pull Request Process
  • Code of Conduct
  • Getting Help
• Development Setup
  • Prerequisites
  • Installation from Source
  • Running Tests
  • EEG And Session Contracts
  • EEGLAB Core Parity Matrix
  • EEGLAB Final Standalone Parity Matrix
  • Building Documentation
  • Debugging Tips
  • Release Process
  • Common Issues
  • Getting Help
• Releasing
  • The version lives in one place
  • Release from your machine
  • What the workflow does
  • If a tag was pushed at a bad commit
  • Docker image
• Changelog
  • Unreleased
  • Version 0.3.0

Five-Minute Script

Load the checked-in tutorial dataset, run two common preprocessing steps, and keep the replayable command strings.

from pathlib import Path
from eegprep import pop_eegfiltnew, pop_loadset, pop_resample, pop_saveset

sample = Path("sample_data") / "eeglab_data.set"
EEG = pop_loadset(sample)

EEG, filter_com = pop_eegfiltnew(EEG, locutoff=1.0, hicutoff=40.0, return_com=True)
EEG, resample_com = pop_resample(EEG, 64, return_com=True)
pop_saveset(EEG, Path("sample_data") / "eeglab_data_preprocessed.set")

print(filter_com)
print(resample_com)

Where EEGLAB Users Should Go First

If you know EEGLAB as…	Start here in EEGPrep
EEG / ALLEEG / CURRENTSET	Concepts Guide
GUI menus plus MATLAB command history	GUI and Console Together, GUI Tutorials, and Interactive Console
pop_* scripts	Scripting Workflows and Interactive Pop Workflows
clean_rawdata, FIRFilt, ICLabel, DIPFIT, EEG-BIDS	Bundled Plugins and Extensions
EEGBrowser and visual rejection	EEGBrowser Workflows
STUDY workflows	STUDY Workflows
MNE-Python interop	MNE-Python Integration

Core Maintainers

• Arnaud Delorme, UCSD, CA, USA

• Suraj Ranganath, UCSD, CA, USA

• Christian Kothe, Intheon, CA, USA

• Bruno Aristimunha Pinto, Inria, France

Indices

• Index

• Module Index

• Search Page