The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines

Clayson, Peter E.; Baldwin, Scott A.; Rocha, Harold A.; Larson, Michael J.

doi:10.1016/j.neuroimage.2021.118712

Cited by 67 publications

(83 citation statements)

References 70 publications

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“…Each pipeline represented a common and legitimate way that ERN could be analyzed. Different combinations of processing and analysis choices led to differences in the strength of associations between ERN and post-error slowing, relationships between ERN and gender, raw amplitudes of ERN scores, and the psychometric internal consistency and test-retest reliability of ERN measurements (see also, Clayson et al, 2021c;Klawohn et al, 2020). Taken together, seemingly innocuous choices about data analysis can impact a study's conclusions in important ways (e.g., strength or direction of an observed relationship, psychometric reliability of outcome measurements).…”

Section: Preregistrationmentioning

confidence: 99%

“…Notably, the tools for estimating subject-level reliability are openly accessible via the ERP Reliability Analysis Toolbox (Clayson & Miller, 2017a), which also includes the capability to estimate test-retest reliability (Clayson et al, 2021e) and the internal consistency of difference scores (Clayson et al, 2021a). Estimates of data quality and psychometric reliability can guide decisions about analysis and processing pipelines (Clayson, 2020;Clayson et al, 2021c;Sandre et al, 2020).…”

Section: Data Analysis and Pipeline Sharingmentioning

confidence: 99%

“…The purpose of this special issue was to gather resources that identify the idiosyncratic considerations and implications of open science practices specifically for studies of human EEG and to promote good scientific practices in the application of these practices to EEG. We chose human EEG as one area of psychophysiology where there is growing concern about processes, analytic pipelines, and decision points that can influence study replicability (e.g., Clayson et al, 2021c;Luck & Gaspelin, 2017), though the principles presented throughout the special issue could apply to any area of psychophysiological research.…”

mentioning

confidence: 99%

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Open science in human electrophysiology

Clayson

Keil

Larson

2022

International Journal of Psychophysiology

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Section: Preregistrationmentioning

confidence: 99%

Section: Data Analysis and Pipeline Sharingmentioning

confidence: 99%

mentioning

confidence: 99%

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Open science in human electrophysiology

Clayson

Keil

Larson

2022

International Journal of Psychophysiology

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“…Studies examining more subtle effect sizes will be more vulnerable to differences in cleaning efficacy, for example, differences between healthy and clinical populations or treatment effects (Rogasch et al, 2020). Effect sizes have also been shown to vary depending on the cleaning pipeline used, suggesting optimal pipeline selection is important (Clayson, Baldwin, et al, 2021; Robbins et al, 2020; Rogasch et al, 2020). Given the benefits of MWF_wICA for cleaning efficacy and its consistently good performance in the detection of differences between experimental conditions, we recommend the use of the RELAX pipeline with MWF_wICA as a default effective cleaning pipeline for ERP research when no rationale exists to prefer another variation of RELAX.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the positive indications noted in the previous paragraph, the primary reason to effectively clean EEG data is to enhance the detection of true experimental outcomes (Clayson, Baldwin, et al, 2021). Based on our findings, we cannot recommend a single best pipeline for all applications.…”

Section: Discussionmentioning

confidence: 99%

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 2: Application to Event-Related Potentials

Bailey

Hill

Biabani

et al. 2022

Preprint

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Electroencephalography (EEG) is commonly used to examine neural activity time-locked to the presentation of a stimulus, referred to as an Event-Related Potential (ERP). However, EEG is also influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning can reduce artifacts, but often requires time-consuming manual decisions. Most automated cleaning methods require frequencies <1Hz to be filtered out of the data, so are not recommended for ERPs (which often contain <1Hz frequencies). In our companion article, we introduced RELAX (the Reduction of Electroencephalographic Artifacts), an automated and modular cleaning pipeline that reduces artifacts with Multiple Wiener Filtering (MWF) and/or wavelet enhanced independent component analysis (wICA) applied to artifact components detected with ICLabel (wICA_ICLabel) (Bailey et al., 2022). To evaluate the suitability of RELAX for data cleaning prior to ERP analysis, multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines. Cleaning performance was compared across a range of artifact cleaning metrics and in the amount of variance in ERPs explained by different conditions in a Go-Nogo task. RELAX with MWF and wICA_ICLabel cleaned the data the most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect experimental effects better for some ERP measures. Importantly, RELAX can high-pass filter data at 0.25Hz, so is applicable to analyses involving ERPs. The pipeline is easy to implement via EEGLAB in MATLAB and is freely available on GitHub. Given its performance, objectivity, and ease of use, we recommend RELAX for EEG data cleaning.

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Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children

Bagdasarov,

Roberts,

Brunet

et al. 2023

Brain Topogr

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The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates – whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity – during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the -64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same -64 to 108 ms period (i.e., error-related microstate 3), and to greater anxiety risk as measured by parent-reported behavioral inhibition. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.

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The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines

Cited by 67 publications

References 70 publications

Open science in human electrophysiology

Open science in human electrophysiology

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 2: Application to Event-Related Potentials

Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children

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