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

Haijing Huang^1,2, Adriano H Moffa^1,2, Colleen Loo^1,2

¹Discipline of Psychiatry and Mental Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia.

Psychophysiology

|November 28, 2025

Summary

This summary is machine-generated.

Preprocessing electroencephalography (EEG) data for working memory tasks requires careful pipeline selection. Optimal EEG signal quality depends on specific research goals, as no single pipeline excels across all metrics.

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Area of Science:

Cognitive Neuroscience
Neuroscience
Signal Processing

Background:

Working memory (WM) is crucial for cognitive functions and often assessed using n-back tasks.
Electroencephalography (EEG) is widely used to study WM, but preprocessing significantly impacts event-related potentials (ERPs).
Existing research lacks systematic comparisons of EEG preprocessing pipelines for n-back tasks.

Purpose of the Study:

To systematically compare 43 EEG preprocessing pipelines for n-back tasks.
To evaluate the impact of filtering, Independent Component Analysis, and re-referencing on ERPs (P300, N200, P200).
To assess data quality across target trials, difference waveforms, and signal-to-noise ratio (SNR) consistency in clinical populations.

Main Methods:

A multiverse analysis of 43 EEG preprocessing pipelines was conducted.
Pipelines varied in high-pass filtering (0.5-4 Hz), low-pass filtering (10-100 Hz), ICA, and re-referencing (average, REST, linked-mastoids).
Data from 164 participants across three n-back datasets (2-back, 3-back) were analyzed.

Main Results:

No single pipeline was optimal across all data quality dimensions.
A pipeline with 0.5 Hz high-pass, 100 Hz low-pass filtering, and REST referencing showed strong SNR and target-nontarget discrimination.
0.5 Hz high-pass filtering specifically improved target vs. nontarget discrimination.
Pipeline choices influenced ERP outcomes at individual and group levels, affecting amplitude preservation and reliability.

Conclusions:

EEG preprocessing pipeline selection critically impacts n-back task ERP data quality and interpretation.
Specific preprocessing choices, such as high-pass filtering and referencing, substantially influence results.
Tailoring preprocessing pipelines to research objectives is essential for robust and reliable EEG findings in WM studies.