Key result
sEEGnal automated pipeline matches expert EEG preprocessing performance while reducing variability.
Why the study?
EEG preprocessing often relies on time-consuming, expert-driven semi-automatic pipelines that limit scalability and reproducibility across large datasets.
sEEGnal provides a robust, scalable, and consistent automated solution for EEG preprocessing that performs comparably to human experts.
Answers are grounded in this paper and cite their sources.
May enable consistent automated EEG preprocessing in research; leaves open prospective validation before clinical neurophysiology adoption.
Electroencephalography (EEG) preprocessing is a critical yet time-consuming step that often relies on expert-driven, semi-automatic pipelines, limiting scalability and reproducibility across large datasets. In this work, we present sEEGnal, a fully automated and modular pipeline for EEG preprocessing designed to produce outputs comparable to expert-driven preprocessing while ensuring consistency and computational efficiency. The pipeline integrates three main modules: data standardisation following the EEG extension of the Brain Imaging Data Structure (BIDS), bad channel detection, and artefact identification, combining physiologically grounded criteria with independent component analysis and ICLabel-based classification. Performance was evaluated against manual preprocessing performed by EEG experts at two complementary levels: preprocessing metadata (bad channels, artefact duration, and rejected components) and EEG-derived measures. In addition, test-retest analyses were conducted to assess the stability of the pipeline across repeated recordings. Results show that sEEGnal achieves performance comparable to expert-driven preprocessing while preserving key neurophysiological features. Furthermore, the pipeline demonstrates reduced variability and increased consistency compared to human experts. These findings support sEEGnal as a robust and scalable solution for automated EEG preprocessing in both research and large-scale applications.
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Ramírez‐Toraño et al. (2026) studied EEG preprocessing. sEEGnal automated EEG preprocessing pipeline vs. expert-driven manual preprocessing was evaluated on preprocessing metadata (bad channels, artefact duration, and rejected components) and EEG-derived measures. The sEEGnal automated EEG preprocessing pipeline achieved performance comparable to expert-driven preprocessing while preserving key neurophysiological features and demonstrating reduced variability.
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