No Single Best Pipeline: Multiverse Analysis of EEG Preprocessing for N‐Back Working Memory Tasks

Working memory (WM) involves temporary information maintenance and manipulation, widely assessed using n‐back tasks. This study systematically compared forty‐three electroencephalography (EEG) preprocessing pipelines, varying in high‐pass filtering, low‐pass filtering, Independent Component Analysis, and re‐referencing methods, to evaluate their impact on event‐related potentials (ERP) during the n‐back (2‐back and 3‐back) task across three previously published datasets with a total of 164 participants. The primary outcome was ERP data quality of the P300 measured at the Pz across preprocessing pipelines. A composite signal quality score combining N200 at Fz and P300 and P200 at Pz was evaluated across all pipelines as a secondary analysis. Data quality was evaluated across three dimensions: target trials signal quality, target‐nontarget difference waveforms, and group‐level signal‐to‐noise ratios (SNR) consistency between major depressive disorder (MDD) and health control participants. No preprocessing pipeline performed optimally across all assessment dimensions. The combination of 0.5 Hz high‐pass filtering, 100 Hz low‐pass filtering, and REST referencing showed strengths in SNR optimization, ranking highly for target trial quality, target‐nontarget difference waveforms, and group‐level analyses. However, other pipelines demonstrated advantages for amplitude preservation or measurement reliability (standardized measurement error). High‐pass filtering and referencing choices substantially influenced ERP outcomes at both individual and group levels. 0.5 Hz high‐pass filtering alone performed optimally for target versus non‐target discrimination. This multiverse analysis demonstrates that preprocessing parameters should be tailored to specific research objectives, as methodological decisions influence EEG data quality and interpretation.