Open-source Software and Pipelines for Generalizable and Reproducible EEG Analysis: An Overview
Abstract
Reproducibility, replicability, robustness, and generalizability are essential but distinct requirements for reliable electroencephalography (EEG) research. Achieving these goals remains challenging because EEG datasets vary substantially across acquisition systems, file formats, experimental paradigms, participant populations, event annotations, and recording conditions. In addition, differences in preprocessing choices, software implementations, parameter settings, and computational environments can substantially influence analytical results. This overview examines the standards, open-source software, and computational pipelines that support transparent and generalizable EEG analysis. We discuss the role of the Brain Imaging Data Structure (BIDS), Hierarchical Event Descriptors (HED), FAIR data principles, and community reporting guidelines in improving data interoperability and reuse. We review major EEG analysis platforms, including Brainstorm, EEGLAB, FieldTrip, MNE-Python, and SPM, as well as automated preprocessing and quality-control pipelines. We also describe supporting infrastructure for dataset validation, curation, standardized event annotation, and scalable cohort-level analysis. Finally, we provide practical recommendations emphasizing standardized data organization, complete reporting of analysis parameters and software environments, code sharing, preregistration, automated quality control, and robustness analyses across plausible pipelines. Together, these practices provide a foundation for EEG findings that are more transparent, reproducible, and generalizable across datasets, laboratories, and analytical tools.
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BibTeX
@misc{medani2026opensource,
title = {{Open-source Software and Pipelines for Generalizable and Reproducible EEG Analysis: An Overview}},
author = {Takfarinas Medani and Raymundo Cassani},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.34047},
eprint = {2610.34047},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.34047},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Open-source Software and Pipelines for Generalizable and Reproducible EEG Analysis: An Overview
AU - Takfarinas Medani
AU - Raymundo Cassani
PY - 2026
DA - 2026/10/25
PB - arXiv
DO - 10.48550/arxiv.2610.34047
UR - https://arxiv.org/abs/2610.34047
AB - Reproducibility, replicability, robustness, and generalizability are essential but distinct requirements for reliable electroencephalography (EEG) research. Achieving these goals remains challenging because EEG datasets vary substantially across acquisition systems, file formats, experimental paradigms, participant populations, event annotations, and recording conditions. In addition, differences in preprocessing choices, software implementations, parameter settings, and computational environments can substantially influence analytical results. This overview examines the standards, open-source software, and computational pipelines that support transparent and generalizable EEG analysis. We discuss the role of the Brain Imaging Data Structure (BIDS), Hierarchical Event Descriptors (HED), FAIR data principles, and community reporting guidelines in improving data interoperability and reuse. We review major EEG analysis platforms, including Brainstorm, EEGLAB, FieldTrip, MNE-Python, and SPM, as well as automated preprocessing and quality-control pipelines. We also describe supporting infrastructure for dataset validation, curation, standardized event annotation, and scalable cohort-level analysis. Finally, we provide practical recommendations emphasizing standardized data organization, complete reporting of analysis parameters and software environments, code sharing, preregistration, automated quality control, and robustness analyses across plausible pipelines. Together, these practices provide a foundation for EEG findings that are more transparent, reproducible, and generalizable across datasets, laboratories, and analytical tools.
ER -