User Guide

Welcome to the eegprep User Guide! This comprehensive guide provides practical documentation for using eegprep in your EEG research and analysis workflows.

Whether you’re just getting started with eegprep or looking to master advanced preprocessing techniques, this guide has you covered. We’ve organized the documentation into logical sections to help you find what you need quickly.

Learning Path

We recommend following this learning path based on your experience level:

Beginner

1. Start with Installation to set up eegprep

2. Follow the Quick Start guide for a 5-minute introduction

3. Read Preprocessing Pipeline to understand the preprocessing workflow

Intermediate

1. Explore Configuration for parameter tuning

2. Learn BIDS Workflow for batch processing

3. Review Preprocessing Pipeline for detailed step-by-step information

Advanced

1. Master Advanced Topics for custom pipelines

2. Explore Configuration for advanced settings

3. Integrate with Advanced Topics for MNE-Python and parallel processing

Getting Started

• Installation
  • System Requirements
  • Installation Methods
  • Optional Dependencies
  • Verification
  • Troubleshooting
  • Getting Help
  • Next Steps
• Quick Start
  • Basic Preprocessing (5-Minute Example)
  • Loading EEG Data
  • Running Preprocessing
  • Saving Results
  • Visualization
  • Complete Workflow Example
  • Common Tasks
  • Next Steps

Core Concepts

• Preprocessing Pipeline
  • Pipeline Overview
  • Pipeline Steps
  • Pipeline Visualization
  • Parameter Tuning
  • Quality Control
  • Common Issues and Solutions
  • Next Steps
• Configuration
  • EEG_OPTIONS Overview
  • Common Configuration Parameters
  • Creating Custom Preprocessing Chains
  • Advanced Settings
  • Configuration Files
  • Parameter Recommendations
  • Troubleshooting Configuration
  • Next Steps

Data Workflows

• BIDS Workflow
  • BIDS Dataset Structure
  • Loading BIDS Data
  • Running Batch Preprocessing
  • Output Structure
  • Integration with Other Tools
  • Working with BIDS Metadata
  • Common BIDS Workflows
  • Troubleshooting BIDS Workflows
  • Best Practices
  • Next Steps

Advanced Topics

• Advanced Topics
  • Custom Preprocessing Chains
  • Extending the Pipeline
  • Integration with MNE-Python
  • Parallel Processing
  • Performance Optimization
  • Best Practices
  • Next Steps

Quick Reference

Common Tasks

• Installation - Install eegprep

• Quick Start - Load, preprocess, and save EEG data

• Preprocessing Pipeline - Understand preprocessing steps

• BIDS Workflow - Process BIDS datasets

• Configuration - Configure preprocessing parameters

• Advanced Topics - Create custom pipelines

Key Functions

• eegprep.pop_loadset() - Load EEG data

• eegprep.pop_saveset() - Save EEG data

• eegprep.clean_artifacts() - Comprehensive artifact removal

• eegprep.iclabel() - Classify ICA components

• eegprep.pop_resample() - Resample data

• eegprep.pop_eegfiltnew() - Filter data

• eegprep.bids_preproc() - Batch process BIDS datasets

Configuration

• eegprep.EEG_OPTIONS - Configuration object

• Preprocessing parameters

• Custom preprocessing chains

Integration

• MNE-Python integration

• EEGLAB compatibility

• BIDS support

Documentation Structure

Installation

Complete installation guide covering system requirements, installation methods, optional dependencies, verification, and troubleshooting.

Quick Start

5-minute introduction to eegprep with practical examples covering loading data, preprocessing, saving results, and visualization.

Preprocessing Pipeline

Detailed overview of the preprocessing pipeline including all steps, parameter tuning, quality control, and common issues.

BIDS Workflow

Guide to working with BIDS-formatted datasets including loading, batch processing, output structure, and integration with other tools.

Configuration

Comprehensive guide to configuring eegprep including EEG_OPTIONS, common parameters, custom preprocessing chains, and advanced settings.

Advanced Topics

Advanced topics for experienced users including custom pipelines, extending the pipeline, MNE-Python integration, parallel processing, and performance optimization.

Key Concepts

EEG Data Structure

eegprep uses the EEGobj class to represent EEG data, which is compatible with EEGLAB format.

Preprocessing Pipeline

The preprocessing pipeline consists of sequential steps: channel selection, artifact removal, channel interpolation, resampling, filtering, ICA decomposition, and component classification.

BIDS Format

Brain Imaging Data Structure (BIDS) is a standardized format for organizing neuroimaging data, enabling consistent and reproducible analysis.

ICA Decomposition

Independent Component Analysis (ICA) decomposes EEG data into independent components, which can be classified as brain activity or artifacts.

Component Classification

ICLabel automatically classifies ICA components into categories such as brain, muscle, eye, heart, line noise, and channel noise.

Getting Help

If you need help:

1. Check the relevant section in this guide

2. Review the API Reference documentation

3. Visit the GitHub Issues page

4. Check the GitHub Discussions page

Contributing

We welcome contributions! If you find issues or have suggestions for improving the documentation, please:

1. Open an issue on GitHub

2. Submit a pull request with improvements

3. Share your feedback in GitHub Discussions

License

eegprep is released under the GNU General Public License v3.0. See the LICENSE file for details.

Citation

If you use eegprep in your research, please cite:

@software{eegprep2024,
  title={eegprep: A Python package for EEG preprocessing},
  author={SCCN},
  year={2024},
  url={https://github.com/sccn/eegprep}
}

Acknowledgments

eegprep is built on the foundations of EEGLAB and incorporates algorithms and methods from the EEG research community. We acknowledge the contributions of all researchers and developers who have contributed to EEG analysis methods.