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This study systematically compared forty\u2010three electroencephalography (EEG) preprocessing pipelines, varying in high\u2010pass filtering, low\u2010pass filtering, Independent Component Analysis, and re\u2010referencing methods, to evaluate their impact on event\u2010related potentials (ERP) during the n\u2010back (2\u2010back and 3\u2010back) 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\u00a0signal quality, target\u2010nontarget difference waveforms, and group\u2010level signal\u2010to\u2010noise 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\u2009Hz high\u2010pass filtering, 100\u2009Hz low\u2010pass filtering, and REST referencing showed strengths in SNR optimization, ranking highly for target trial quality, target\u2010nontarget difference waveforms, and group\u2010level analyses. However, other pipelines demonstrated advantages for amplitude preservation or measurement reliability (standardized measurement error). High\u2010pass filtering and referencing choices substantially influenced ERP outcomes at both individual and group levels. 0.5\u2009Hz high\u2010pass filtering alone performed optimally for target versus non\u2010target discrimination. This multiverse analysis demonstrates that preprocessing parameters should be tailored to specific research objectives, as methodological decisions influence EEG data quality and interpretation.<\/jats:p>","DOI":"10.1111\/psyp.70197","type":"journal-article","created":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T04:21:38Z","timestamp":1764390098000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["No Single Best Pipeline: Multiverse Analysis of EEG Preprocessing for N\u2010Back Working Memory Tasks"],"prefix":"10.1111","volume":"62","author":[{"given":"Haijing","family":"Huang","sequence":"first","affiliation":[{"name":"Discipline of Psychiatry and Mental Health, Faculty of Medicine and Health, School of Clinical Medicine University of New South Wales  Sydney New South Wales Australia"},{"name":"Black Dog Institute  Sydney New South Wales 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