autoclean-eeg 2.0.2

A modular framework for automated EEG data processing, built on MNE-Python

AutoClean EEG

A modular framework for automated EEG data processing, built on MNE-Python.

Features

• Framework for automated EEG preprocessing with "lego block" modularity
• Support for multiple EEG paradigms (ASSR, Chirp, MMN, Resting State)
• BIDS-compatible data organization and comprehensive quality control
• Extensible plugin system for file formats, montages, and event processing
• Research-focused workflow: single file testing → parameter tuning → batch processing
• Detailed output: logs, stage files, metadata, and quality control visualizations

Installation

pip install autoclean-eeg

For development installation:

git clone https://github.com/cincibrainlab/autoclean_pipeline.git
cd autoclean-eeg
uv tool install -e --upgrade ".[dev]"

Quick Start

AutoClean EEG offers two approaches for building custom EEG processing workflows:

Option 1: Python Task Files (Recommended for New Users)

Create simple Python files that combine configuration and processing logic:

# my_task.py
from typing import Any, Dict
from autoclean.core.task import Task

# Embedded configuration
config = {
    'resample_step': {'enabled': True, 'value': 250},
    'filtering': {'enabled': True, 'value': {'l_freq': 1, 'h_freq': 100}},
    'ICA': {'enabled': True, 'value': {'method': 'picard'}},
    'epoch_settings': {'enabled': True, 'value': {'tmin': -1, 'tmax': 1}}
}

class MyRestingTask(Task):
    def __init__(self, config: Dict[str, Any]):
        self.settings = globals()['config']
        super().__init__(config)
    
    def run(self) -> None:
        self.import_raw()
        self.run_basic_steps(export=True)
        self.run_ica(export=True)
        self.create_regular_epochs(export=True)

# Use your custom task
from autoclean import Pipeline

pipeline = Pipeline(output_dir="/path/to/output")
pipeline.add_task("my_task.py")
pipeline.process_file("/path/to/data.raw", task="MyRestingTask")

Option 2: Traditional YAML Configuration

For complex workflows or when you prefer separate config files:

from autoclean import Pipeline

# Initialize pipeline with YAML configuration
pipeline = Pipeline(
    output_dir="/path/to/output"
)

# Process using built-in tasks
pipeline.process_file(
    file_path="/path/to/test_data.raw", 
    task="rest_eyesopen"
)

Typical Research Workflow

1. Test single file to validate task and tune parameters
2. Review results in output directories and adjust as needed
3. Process full dataset using batch processing

# Batch processing (works with both approaches)
pipeline.process_directory(
    directory="/path/to/dataset",
    task="MyRestingTask",  # or built-in task name
    pattern="*.raw"
)

Key Benefits of Python Task Files:

• Simpler: No separate YAML files to manage
• Self-contained: Configuration and logic in one file
• Flexible: Optional export=True parameters control file outputs
• Intuitive: Pandas-like API with sensible defaults

Documentation

Full documentation is available at https://cincibrainlab.github.io/autoclean_pipeline/

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this software in your research, please cite:

@software{autoclean_eeg,
  author = {Gammoh, Gavin, Pedapati, Ernest, and Grace Westerkamp},
  title = {AutoClean EEG: Automated EEG Processing Pipeline},
  year = {2024},
  publisher = {GitHub},
  url = {[https://github.com/yourusername/autoclean-eeg](https://github.com/cincibrainlab/autoclean_pipeline/)}
}

Acknowledgments

• Cincinnati Children's Hospital Medical Center
• Built with MNE-Python