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Peer-reviewedCC BYBehavior Research Methods

PyLossless: A non-destructive EEG processing pipeline

  • Scott Huberty1
  • James A. Desjardins2
  • Tyler Collins2
  • Mayada Elsabbagh1
  • Christian O’Reilly3,4,5
5 affiliations
  1. 1Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
  2. 2Compute Ontario, St. Catharines, Canada
  3. 3Artificial Intelligence Institute of South Carolina, University of South Carolina, Columbia, SC, USA
  4. 4Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC, USA
  5. 5Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA
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Abstract

EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated preprocessing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated preprocessing pipelines typically remove data considered artifacts and return a subset of irreversibly transformed signals. This approach obfuscates preprocessing decisions and often makes it impossible to recover the original data or modify the preprocessing steps. Further, it complicates collaboration among research teams working on a common dataset, as different analyses may require specific preprocessing steps. Given the large amount of resources devoted to collecting EEG, tools that can efficiently and transparently preprocess data are greatly needed. PyLossless addresses this need by creating a non-destructive, automated preprocessing pipeline that maintains the continuous EEG structure. It offers a user-friendly API, is well documented, tested through continuous integration, easily deployable, and integrates with the popular MNE-Python environment. The pipeline also provides a browser-based quality control review (QCR) dashboard that allows researchers to visualize and edit automated artifact flags for sensors, time periods, and independent components. The end product of PyLossless is a lossless annotated data state that can be shared and used with analysis-specific artifact rejection policies, allowing for an optimal balance between flexibility and standardization.

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Cite

BibTeXRIS
BibTeX
@article{huberty2026pylossless,
  title = {{PyLossless: A non-destructive EEG processing pipeline}},
  author = {Scott Huberty and James A. Desjardins and Tyler Collins and Mayada Elsabbagh and Christian O’Reilly},
  journal = {Behavior Research Methods},
  year = {2026},
  month = jul,
  doi = {10.3758/s13428-026-02997-z},
  url = {https://doi.org/10.3758/s13428-026-02997-z},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - PyLossless: A non-destructive EEG processing pipeline
AU  - Scott Huberty
AU  - James A. Desjardins
AU  - Tyler Collins
AU  - Mayada Elsabbagh
AU  - Christian O’Reilly
PY  - 2026
DA  - 2026/07/06
JO  - Behavior Research Methods
DO  - 10.3758/s13428-026-02997-z
UR  - https://doi.org/10.3758/s13428-026-02997-z
AB  - EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated preprocessing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated preprocessing pipelines typically remove data considered artifacts and return a subset of irreversibly transformed signals. This approach obfuscates preprocessing decisions and often makes it impossible to recover the original data or modify the preprocessing steps. Further, it complicates collaboration among research teams working on a common dataset, as different analyses may require specific preprocessing steps. Given the large amount of resources devoted to collecting EEG, tools that can efficiently and transparently preprocess data are greatly needed. PyLossless addresses this need by creating a non-destructive, automated preprocessing pipeline that maintains the continuous EEG structure. It offers a user-friendly API, is well documented, tested through continuous integration, easily deployable, and integrates with the popular MNE-Python environment. The pipeline also provides a browser-based quality control review (QCR) dashboard that allows researchers to visualize and edit automated artifact flags for sensors, time periods, and independent components. The end product of PyLossless is a lossless annotated data state that can be shared and used with analysis-specific artifact rejection policies, allowing for an optimal balance between flexibility and standardization.
SN  - 1554-351X
SN  - 1554-3528
ER  -