References and Citations#

Key Publications#

EEG Preprocessing Methods#

The following papers describe key preprocessing methods implemented in EEGPrep:

Artifact Removal and Cleaning

  • Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21.

    • Foundational paper for EEGLAB and many preprocessing techniques

  • Kothe, C. A., & Makeig, S. (2013). BCILAB: a platform for brain–computer interface development. Journal of Neural Engineering, 10(5), 056014.

    • Describes ASR (Artifact Subspace Reconstruction) algorithm

  • Onton, J., Westerfield, M., Townsend, J., & Makeig, S. (2006). Imaging human EEG dynamics using independent component analysis. Neuroscience & Biobehavioral Reviews, 30(6), 808-822.

    • ICA for EEG analysis

Independent Component Analysis (ICA)

  • Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430.

    • Comprehensive ICA review

  • Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-1159.

    • Infomax ICA algorithm

ICLabel Component Classification

  • Pion-Tonachini, L., Kreutz-Delgado, K., & Makeig, S. (2019). ICLabel: Automated electroencephalographic independent component classification, labeling and brain source estimation. NeuroImage, 198, 181-197.

    • Deep learning-based IC classification

BIDS Format

  • Gorgolewski, K. J., Auer, T., Calhoun, V. D., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044.

    • BIDS specification paper

  • Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., et al. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.

    • EEG-BIDS extension

Signal Processing

  • Widmann, A., Schröger, E., & Maess, B. (2015). Digital filter design for electrophysiological data–a practical approach. Journal of Neuroscience Methods, 250, 34-46.

    • Filter design for EEG

External Resources#

Tutorials and Documentation#

EEG Analysis Tutorials

Signal Processing

Machine Learning

Forums and Communities#

GitHub

EEG Community Lists

Stack Overflow

Reddit

Datasets#

Public EEG Datasets

Citation Information#

How to Cite EEGPrep#

If you use EEGPrep in your research, please cite it as:

BibTeX:

@software{eegprep2024,
  title={EEGPrep: A comprehensive Python EEG preprocessing pipeline},
  author={EEGPrep Contributors},
  year={2024},
  url={https://github.com/sccn/eegprep}
}

APA Format:

EEGPrep Contributors. (2024). EEGPrep: A comprehensive Python EEG preprocessing pipeline. Retrieved from sccn/eegprep

Chicago Format:

EEGPrep Contributors. “EEGPrep: A comprehensive Python EEG preprocessing pipeline.” Accessed 2024. sccn/eegprep.

Citing Dependencies#

If you use specific algorithms, please also cite the original papers:

For ASR (Artifact Subspace Reconstruction):

@article{kothe2013bcilab,
  title={BCILAB: a platform for brain--computer interface development},
  author={Kothe, Christian A and Makeig, Scott},
  journal={Journal of Neural Engineering},
  volume={10},
  number={5},
  pages={056014},
  year={2013},
  publisher={IOP Publishing}
}

For ICLabel:

@article{pion2019iclabel,
  title={ICLabel: Automated electroencephalographic independent component classification, labeling and brain source estimation},
  author={Pion-Tonachini, Luca and Kreutz-Delgado, Kenneth and Makeig, Scott},
  journal={NeuroImage},
  volume={198},
  pages={181--197},
  year={2019},
  publisher={Elsevier}
}

For EEGLAB:

@article{delorme2004eeglab,
  title={EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis},
  author={Delorme, Arnaud and Makeig, Scott},
  journal={Journal of Neuroscience Methods},
  volume={134},
  number={1},
  pages={9--21},
  year={2004},
  publisher={Elsevier}
}

Acknowledgments#

Contributors#

EEGPrep is developed and maintained by SCCN contributors and the EEG research community. We thank all contributors who have helped improve the project through code contributions, bug reports, and feedback.

Funding#

EEGPrep development has been supported by:

  • SCCN and EEG community contributors

  • Open-source software initiatives

  • Academic institutions

Inspiration and Acknowledgments#

EEGPrep builds upon the excellent work of:

  • EEGLAB: For pioneering EEG preprocessing and analysis tools

  • MNE-Python: For comprehensive neuroimaging analysis

  • Fieldtrip: For robust signal processing methods

  • Brainstorm: For user-friendly neuroimaging software

We acknowledge the neuroscience and signal processing communities for their contributions to EEG analysis methods.

Getting Help with References#

  • Check the User Guide for implementation details

  • Review Examples for practical examples

  • Search GitHub Issues for related discussions

  • Contact the maintainers for citation questions

For more information about EEG analysis methods, see the Glossary for terminology definitions.