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Related Experiment Video

Updated: Jul 5, 2026

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
06:51

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

Published on: June 6, 2025

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Federico Ramírez-Toraño1, Christoffer Hatlestad-Hall2, Ainar Drews3

  • 1Center for Cognitive and Computational Neuroscience (C3N), Universidad Complutense de Madrid, Madrid, Spain.

Computers in Biology and Medicine
|July 3, 2026
PubMed
Summary

sEEGnal offers automated electroencephalography (EEG) preprocessing, matching expert performance for consistency and efficiency. This scalable solution improves reproducibility in large-scale EEG research.

Keywords:
Artefact detectionAutomated pipelineBIDSBad channel detectionEEG preprocessing

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) preprocessing is crucial but often manual, hindering large-scale studies.
  • Existing pipelines lack consistency and scalability, impacting reproducibility.
  • Automated solutions are needed to standardize EEG data processing.

Purpose of the Study:

  • To introduce sEEGnal, a fully automated and modular EEG preprocessing pipeline.
  • To achieve preprocessing outputs comparable to expert-driven methods.
  • To ensure consistency, computational efficiency, and scalability in EEG analysis.

Main Methods:

  • Data standardization using the EEG extension of the Brain Imaging Data Structure (BIDS).
  • Bad channel detection and artifact identification using physiological criteria, ICA, and ICLabel.
  • Performance evaluation against manual expert preprocessing and test-retest stability analysis.

Main Results:

  • sEEGnal demonstrated performance comparable to expert-driven preprocessing.
  • The pipeline preserved key neurophysiological features in EEG data.
  • sEEGnal showed reduced variability and increased consistency versus human experts.

Conclusions:

  • sEEGnal provides a robust and scalable solution for automated EEG preprocessing.
  • The pipeline enhances consistency and reproducibility in large-scale EEG research.
  • sEEGnal is suitable for both research and clinical applications.