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

Electrophysiological and behavioral differences of general and food-specific inhibitory control in people with different levels of intuitive eating

Intuitive eating influences health-related behaviors (including calorie and nutritional intake) that are modulated by inhibitory control, producing implications for physical, mental, and emotional health. However, little is known about the relationship between intuitive eating habits and inhibitory control. Therefore, we tested intuitive eating's influence on measures of general and food-related inhibitory control using behavioral and event-related potentials (N2 and P3 components). We included 40 healthy participants: 23 had a higher level of intuitive eating, and 17 had a lower level. They participated in food-specific go/no-go and general go/no-go tasks for which we recorded electroencephalogram data. As expected, in the food-specific go/no-go task, the P3 component amplitude in the lower intuitive eating group was significantly larger than in the higher intuitive eating group; there were no significant between-group differences in the N2 amplitudes or behavioral measures. Moreover, there were no ERP or behavioral difference between groups in the general go/no-go task. Further research is needed to understand the role of positive eating behaviors in food-specific inhibitory control.

Transdiagnostic psychopathology in the light of robust single-trial event-related potentials

Recent evidence indicates that event-related potentials (ERPs) as measured on the electroencephalogram (EEG) are more closely related to transdiagnostic, dimensional measures of psychopathology (TDP) than to diagnostic categories. A comprehensive examination of correlations between well-studied ERPs and measures of TDP is called for. In this study, we recruited 50 patients with emotional disorders undergoing 14 weeks of transdiagnostic group psychotherapy as well as 37 healthy comparison subjects (HC) matched in age and sex. HCs were assessed once and patients three times throughout treatment (N = 172 data sets) with a battery of well-studied ERPs and psychopathology measures consistent with the TDP framework The Hierarchical Taxonomy of Psychopathology (HiTOP). ERPs were quantified using robust single-trial analysis (RSTA) methods and TDP correlations with linear regression models as implemented in the EEGLAB toolbox LIMO EEG. We found correlations at several levels of the HiTOP hierarchy. Among these, a reduced P3b was associated with the general p-factor. A reduced error-related negativity correlated strongly with worse symptomatology across the Internalizing spectrum. Increases in the correct-related negativity correlated with symptoms loading unto the Distress subfactor in the HiTOP. The Flanker N2 was related to specific symptoms of Intrusive Cognitions and Traumatic Re-experiencing and the mismatch negativity to maladaptive personality traits at the lowest levels of the HiTOP hierarchy. Our study highlights the advantages of RSTA methods and of using validated TDP constructs within a consistent framework. Future studies could utilize machine learning methods to predict TDP from a set of ERP features at the subject level.

Evaluating a novel high-density EEG sensor net structure for improving inclusivity in infants with curly or tightly coiled hair

Electroencephalography (EEG) is an important tool in the field of developmental cognitive neuroscience for indexing neural activity. However, racial biases persist in EEG research that limit the utility of this tool. One bias comes from the structure of EEG nets/caps that do not facilitate equitable data collection across hair textures and types. Recent efforts have improved EEG net/cap design, but these solutions can be time-intensive, reduce sensor density, and are more difficult to implement in younger populations. The present study focused on testing EEG sensor net designs over infancy. Specifically, we compared EEG data quality and retention between two high-density saline-based EEG sensor net designs from the same company (Magstim EGI, Whitland, UK) within the same infants during a baseline EEG paradigm. We found that within infants, the tall sensor nets resulted in lower impedances during collection, including lower impedances in the key online reference electrode for those with greater hair heights and resulted in a greater number of usable EEG channels and data segments retained during pre-processing. These results suggest that along with other best practices, the modified tall sensor net design is useful for improving data quality and retention in infant participants with curly or tightly-coiled hair.

A registered report of a two-site study of variations of the flanker task: ERN experimental effects and data quality

Error-related negativity is a widely used measure of error monitoring, and many projects are independently moving ERN recorded during a flanker task toward standardization, optimization, and eventual clinical application. However, each project uses a different version of the flanker task and tacitly assumes ERN is functionally equivalent across each version. The routine neglect of a rigorous test of this assumption undermines efforts to integrate ERN findings across tasks, optimize and standardize ERN assessment, and widely apply ERN in clinical trials. The purpose of this registered report was to determine whether ERN shows similar experimental effects (correct vs. error trials) and data quality (intraindividual variability) during three commonly used versions of a flanker task. ERN was recorded from 172 participants during three versions of a flanker task across two study sites. ERN scores showed numerical differences between tasks, raising questions about the comparability of ERN findings across studies and tasks. Although ERN scores from all three versions of the flanker task yielded high data quality and internal consistency, one version did outperform the other two in terms of the size of experimental effects and the data quality. Exploratory analyses of the error positivity (Pe) provided tentative support for the other two versions of the task over the paradigm that appeared optimal for ERN. The present study provides a roadmap for how to statistically compare psychometric characteristics of ERP scores across paradigms and gives preliminary recommendations for flanker tasks to use for ERN- and Pe-focused studies.

The future of psychophysiology, then and now

Since its founding in 1973, Biological Psychology has showcased and provided invaluable support to psychophysiology, a field that has grown and changed enormously. This article discusses some constancies that have remained fundamental to the journal and to the field as well as some important trends. Some aspects of our science have not received due consideration, affecting not only the generalizability of our findings but the way we develop and evaluate our research questions and the potential of our field to contribute to the common good. The article offers a number of predictions and recommendations for the next period of growth of psychophysiology.

Domain specificity of error monitoring: An ERP study in young and older adults

Metacognition refers to the ability to monitor and control one's cognitive processes, which plays an important role in decision-making throughout the lifespan. It is still debated whether metacognitive abilities decline with age. Neuroimaging evidence suggests that metacognition is served by domain-specific mechanisms. These domains may differentially decline with increasing age. The current investigates whether the error-related negativity (ERN) and the error positivity (Pe) which reflect error detection and error awareness, respectively, differ across perceptual and memory domains in young and older adults. In total, 38 young adults and 37 older adults completed a classic Flanker Task (perceptual) and an adapted memory-based version. No difference in ERN amplitude was found between young and older adults and across domains. Perceptual ERN peaked earlier than Memory ERN. Memory ΔERN was larger than Perceptual ΔERN. Pe was smaller in older adults and ΔPe was larger for perceptual than memory flanker. Memory Pe peaked earlier in young as compared to older adults. Multivariate analyses of whole scalp data supported cross-domain differences. During the task, ERN decreased in young but not in older adults. Memory Pe decreased in young adults but increased in older adults while no significant change in perceptual Pe was found. The study's findings suggest that neural correlates of error monitoring differ across cognitive domains. Moreover, it was shown that error awareness declines in old age but its within-task dynamics vary across cognitive domains. Possible mechanisms underlying metacognition impairments in aging are discussed.

The psychometric upgrade psychophysiology needs

Although biological measurements are constrained by the same fundamental psychometric principles as self-report measurements, these essential principles are often neglected in most fields of neuroscience, including psychophysiology. Potential reasons for this neglect could include a lack of understanding of appropriate measurement theory or a lack of accessible software for psychometric analysis. Generalizability theory is a flexible and multifaceted measurement theory that is well suited to handling the nuances of psychophysiological data, such as the often unbalanced number of trials and intraindividual variability of scores of event-related brain potential (ERP) data. The ERP Reliability Analysis Toolbox (ERA Toolbox) was designed for psychophysiologists and is tractable software that can support the routine evaluation of psychometrics using generalizability theory. Psychometrics can guide task refinement, data-processing decisions, and selection of candidate biomarkers for clinical trials. The present review provides an extensive treatment of additional psychometric characteristics relevant to studies of psychophysiology, including validity and validation, standardization, dimensionality, and measurement invariance. Although the review focuses on ERPs, the discussion applies broadly to psychophysiological measures and beyond. The tools needed to rigorously assess psychometric reliability and validate psychophysiological measures are now readily available. With the profound implications that psychophysiological research can have on understanding brain-behavior relationships and the identification of biomarkers, there is simply too much at stake to ignore the crucial processes of evaluating psychometric reliability and validity.

Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology

Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study's findings. These decisions create a "multiverse" of possible outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of a study's findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related brain potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. This review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as an introduction, helping researchers to make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.

Direct and indirect effects of error monitoring on social functioning in a cohort with high-risk and first-episode psychosis

Error monitoring plays a key role in people's adjustment to social life. This study aimed to examine the direct (DE) and indirect effects (IDE) of error monitoring, as indicated by error-related negativity (ERN), on social functioning in a clinical cohort from high-risk (APS) to first-episode psychosis (FEP). This study recruited 100 outpatients and 49 healthy controls (HC). ERN was recorded during a modified flanker task; social functioning was evaluated using the social scale of global functioning. The path analysis was executed using the "lavaan" package. When controlling for age and education, the clinical cohort had a smaller ERN than the HC group (F1, 145 = 19.58, p < 0.001, partial η2 = 0.12, 95%CI: 0.04-0.22). ERN demonstrated no substantial direct impact on current social functioning; however, it manifested indirect influences on social functioning via the disorganization factor of the Positive and Negative Syndrome Scale, both with (standardized IDE: -0.139, p = 0.009) and without (standardized IDE: -0.087, p = 0.018) accounting for the diagnosis, defined as a dummy variable (FEP = 1 and APS = 0) and included as a covariate. These findings suggest that error monitoring, as indicated by ERN, may serve as a potential prognostic indicator of social functioning in patients with psychosis.

EEGManyPipelines: A Large-scale, Grassroots Multi-analyst Study of Electroencephalography Analysis Practices in the Wild

The ongoing reproducibility crisis in psychology and cognitive neuroscience has sparked increasing calls to re-evaluate and reshape scientific culture and practices. Heeding those calls, we have recently launched the EEGManyPipelines project as a means to assess the robustness of EEG research in naturalistic conditions and experiment with an alternative model of conducting scientific research. One hundred sixty-eight analyst teams, encompassing 396 individual researchers from 37 countries, independently analyzed the same unpublished, representative EEG data set to test the same set of predefined hypotheses and then provided their analysis pipelines and reported outcomes. Here, we lay out how large-scale scientific projects can be set up in a grassroots, community-driven manner without a central organizing laboratory. We explain our recruitment strategy, our guidance for analysts, the eventual outputs of this project, and how it might have a lasting impact on the field.

Maximizing signal-to-noise ratio and statistical power in ERP measurement: Single sites versus multi-site average clusters

One important decision in every event-related potential (ERP) experiment is which electrode site(s) to use in quantifying the ERP component of interest. A common approach is to measure the ERP from a single electrode site, typically the site where the ERP component is largest. Alternatively, two or more electrode sites in a given spatial region are averaged together, and the ERP is measured from the resulting multi-site cluster. The goal of the present study was to systematically compare these two measurement approaches across a range of outcome measures and ERP components to determine whether measuring from a single electrode site or an average of multiple sites yields consistently better results. We examined seven common ERP components from the open-source ERP CORE dataset that span a range of neurocognitive processes: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). For each component, we compared ERP amplitude, noise level, signal-to-noise ratio, and effect size at two single electrode sites and four multi-site clusters. We also used a Monte Carlo approach to simulate within-participant and between-groups experiments with known effect magnitudes to compare statistical power at single sites and multi-site clusters. Overall, measuring from a multi-site cluster produced results that were as good as or better than measuring from a single electrode site across analyses and components, indicating that the cluster-based measurement approach may be beneficial in quantifying ERPs from a range of neurocognitive domains.

Normal congruency sequence effects in psychopathology: A behavioral and electrophysiological examination using a confound-minimized design

Clinical studies of adaptive control emphasize the role disruptions in control play in psychopathology. However, many studies used confound-laden designs and examined only one type of psychopathology. Recent studies of event-related potentials (ERPs) suggest that robust congruency sequence effects (CSEs)-a popular index of adaptive control-appear in confound-minimized designs. Thus, the present study sought to determine whether a confound-minimized CSE paradigm could identify adaptive control dysfunction in people with major depressive disorder (MDD), generalized anxiety disorder (GAD), and obsessive-compulsive disorder (OCD). We predicted that participants with MDD and GAD would show smaller ERP CSEs and that participants with OCD would show larger ERP CSEs than healthy controls. Data from 44 people with GAD, 51 people with MDD, 31 people with OCD, and 56 healthy comparison participants revealed normal CSEs as indexed by response times (RTs) and ERPs in the psychopathology groups. Moreover, psychiatric symptoms did not moderate these CSEs. Finally, we observed a strong mean-variance relationship in RT CSEs, such that participants with stronger post-recruitment of control in mean RT scores showed the most consistent post-conflict responses (i.e., the least intraindividual variability). These findings suggest that prior findings from confound-laden tasks indicating altered CSEs in psychopathology stem from processes that are unrelated to adaptive control. Future research should employ experimental designs that isolate these processes to advance our understanding of abnormal CSEs in psychopathology.

Registered replication report of the construct validity of the error-related negativity (ERN): A multi-site study of task-specific ERN correlations with internalizing and externalizing symptoms

Intact cognitive control is critical for goal-directed behavior and is widely studied using the error-related negativity (ERN). A common assumption in such studies is that ERNs recorded during different experimental paradigms reflect the same construct or functionally equivalent processes and that ERN is functionally distinct from other error-monitoring event-related brain potentials (ERPs; error positivity [Pe]), other neurophysiological indices of cognitive control (N2), and even other theoretically unrelated indices (visual N1). The present registered report represents a replication-plus-extension study of the psychometric validity of cognitive control ERPs and evaluated the convergent and divergent validity of ERN, Pe, N2, and visual N1 recorded during flanker, Stroop, and Go/no-go tasks. Data from 182 participants were collected from two study sites, and ERP psychometric reliability and validity were evaluated. Findings supported replication of convergent and divergent validity of ERN, Pe, and ΔPe (error minus correct)-these ERPs correlated more with themselves across tasks than with other ERPs measured during the same task. Convergent validity of ΔERN across tasks was not replicated, despite high internal consistency. ERN strongly correlated with N2 at levels similar or higher than those in support of convergent validity for other ERPs, and the present study failed to provide evidence of divergent validity for ERN and Pe from N2 or N1. ERN and ΔERN were unrelated to internalizing or externalizing symptoms. Findings underscore the importance of considering the psychometric validity of ERPs, as it provides a foundation for interpreting and comparing ERPs across tasks and studies.

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

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.

Estimating statistical power for ERP studies using the auditory N1, Tb, and P2 components

The N1, Tb, and P2 components of the event-related potential (ERP) are thought to reflect the sequential processing of auditory stimuli in the human brain. Despite their extensive use in biological, cognitive, and clinical neuroscience, there are no guidelines for how to appropriately power ERP studies using these components. In the present study, we investigated how the number of trials, number of participants, effect magnitude, and study design influenced statistical power. Using Monte Carlo simulations of ERP data from a passive listening task, we determined the probability of finding a statistically significant effect in 58,900 experiments repeated 1,000 times each. We found that as the number of trials, number of participants, and effect magnitude increased, so did statistical power. We also found that increasing the number of trials had a bigger effect on statistical power for within-subject designs than for between-subject designs, and that within-subject designs required a smaller number of trials and participants to provide the same level of statistical power for a given effect magnitude than between-subject designs. These results show that it is important to carefully consider these factors when designing ERP studies, rather than relying on tradition or anecdotal evidence. To improve the robustness and reproducibility of ERP research, we have built an online statistical power calculator (https://bradleynjack.shinyapps.io/ErpPowerCalculator), which we hope will allow researchers to estimate the statistical power of previous studies, as well as help them design appropriately-powered studies in the future.

Validation of the g.tec Unicorn Hybrid Black wireless EEG system

Although dry and hybrid-style electrode technology has been well validated, systems utilizing these electrodes have not been widely adopted. One reason for this may be that the systems incorporating such technology present limitations that are fundamental to the EEG approach. The g.tec Unicorn Hybrid Black system, a low density Bluetooth EEG amplifier, however, attempts to address many of these limitations to allow greater flexibility to replicate methods used with traditional EEG amplifiers and extend them to more novel applications. The aim of the present investigation was to validate the g.tec Unicorn Hybrid Black amplifier to determine if it provides comparable data to a traditional laboratory-based system when no electrode preparation is utilized or if a saline-based solution is necessary to obtain sufficient signal quality. Stimulus-locked ERPs and EEG power spectrum data were concurrently recorded using both the Unicorn Hybrid Black amplifier and a traditional high-end laboratory-based low-impedance wired system. Findings suggest that the Unicorn Hybrid Black provides valid measures for investigations of frequency spectra even with no conductive solution applied. However, to obtain valid assessments of event-related brain potentials, it appears necessary to use a conductive solution for electrode preparation. This system appears well suited to allow for high-quality and flexible EEG measures available outside of traditional laboratory environments.

Exploration of Categorization and Category-Based Induction on Waste Sorting: A Follow-Up Observation by NeuroSky MindWave

Waste sorting, as an embodiment of behavioral cognition, is regulated by two cognitive processes, namely, Categorization (C) and Category-Based Induction (CBI). This study employed the event-related potential (ERP) technique to assess whether there is a transformation between C and CBI in waste sorting cognition, in order to help individuals establish a correct waste sorting behavior. We reported a case of intervention in waste sorting education with a 58-year-old Chinese woman to discriminate whether such intervention facilitates the transition between C and CBI. The results showed that the waste sorting cognition follows a developmental model as C-CBI-C, where education may help the subject build a cognitive framework for waste sorting by altering inherent misperceptions and filling gaps in classification knowledge. The results also noticed that FN400 is identified as a characteristic waveform in the CBI process, by which it is indicated that the first 4 to 7 days of education is a critical period for establishing the cognitive framework. Through a comparison of the ERP waveforms at different stages of intervention, the results are insightful to help individuals improve their cognition of waste sorting.

Reproducibility in Neuroimaging Analysis: Challenges and Solutions

Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research-one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.

Exploring the association between EEG microstates during resting-state and error-related activity in young children

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 parent-report-measured anxiety risk. 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.

Age-related differences in food-specific inhibitory control: Electrophysiological and behavioral evidence in healthy aging

The number of older adults in the United States is estimated to nearly double from 52 million to 95 million by 2060. Approximately 80-85% of older adults are diagnosed with a chronic health condition. Many of these chronic health conditions are influenced by diet and physical activity, suggesting improved diet and eating behaviors could improve health-related outcomes. One factor that might improve dietary habits in older adults is food-related inhibitory control. We tested whether food-related inhibitory control, as measured via behavioral data (response time, accuracy) and scalp-recorded event-related potentials (ERP; N2 and P3 components), differed between younger and older adults over age 55. Fifty-nine older adults (31 females [52.5%], M_age = 64, SD_age = 7.5) and 114 younger adults (82 females [71.9%], M_age = 20.8) completed two go/no-go tasks, one inhibiting to high-calorie stimuli and one inhibiting to low-calorie stimuli, while electroencephalogram (EEG) data were recorded. Older adults had slower overall response times than younger adults, but this was not specific to either food task. There was not a significant difference in accuracy between younger and older adults, but both groups' accuracy and response times were significantly better during the high-calorie task than the low-calorie task. For both the N2 and P3 ERP components, younger adults had larger no-go ERP amplitudes than older adults, but this effect was not food-specific, reflecting overall generalized lower inhibitory control processing in older adults. P3 amplitude for the younger adults demonstrated a specific food-related effect (greater P3 amplitude for high-calorie no-go than low-calorie no-go) that was not present for older adults. Findings support previous research demonstrating age-related differences in inhibitory control though those differences may not be specific to inhibiting towards food.

Higher amplitudes in steady-state visual evoked potentials driven by square-wave versus sine-wave contrast modulation - A dual-laboratory study

Steady-state visual evoked potentials (ssVEPs) are an established tool for assessing visuocortical responses in visual perception and attention. They have the same temporal frequency characteristics as a periodically modulated stimulus (e.g., in contrast or luminance) that drives them. It has been hypothesized that the amplitude of a given ssVEP may depend on the shape of the stimulus modulation function, but the size and robustness of these effects is not well established. The current study systematically compared the effect of the two most common functions in the ssVEP literature, square-wave and sine-wave functions. Across two laboratories, we presented mid-complex color patterns to 30 participants with square-wave or sine-wave contrast modulation and at different driving frequencies (6 Hz, 8.57 Hz, 15 Hz). When ssVEPs were analyzed independently for the samples, with each laboratory's standard processing pipeline, ssVEP amplitudes in both samples decreased at higher driving frequencies and square-wave modulation evoked higher amplitudes at lower frequencies (i.e., 6 Hz, 8.57 Hz) compared to sine-wave modulation. These effects were replicated when samples were aggregated and analyzed with the same processing pipeline. In addition, when using signal-to-noise ratios as outcome measures, this joint analysis indicated a somewhat weaker effect of increased ssVEP amplitudes to square-wave modulation at 15 Hz. The present study suggests that square-wave modulation should be used in ssVEP research when the goal is to maximize signal amplitude or signal-to-noise ratio. Given effects of modulation function across laboratories, and data processing pipelines, the findings appear robust to differences in data collection and analysis.

RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials

OBJECTIVE

Electroencephalography (EEG) is often used to examine neural activity time-locked to stimuli presentation, referred to as Event-Related Potentials (ERP). However, EEG is influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning reduces artifacts, but often requires time-consuming manual decisions. Most automated methods filter frequencies <1 Hz out of the data, so are not recommended for ERPs (which contain frequencies <1 Hz). Our aim was to test the RELAX (Reduction of Electroencephalographic Artifacts) pre-processing pipeline for use on ERP data.

METHODS

The cleaning performance of multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines across both artifact cleaning metrics and the amount of variance in ERPs explained by different conditions in a Go-Nogo task. Results RELAX with Multi-channel Wiener Filtering (MWF) and wavelet-enhanced independent component analysis applied to artifacts identified with ICLabel (wICA_ICLabel) cleaned data most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect effects better for some ERPs.

CONCLUSIONS

RELAX shows high artifact cleaning performance even when data is high-pass filtered at 0.25 Hz (applicable to ERP analyses).

SIGNIFICANCE

RELAX is easy to implement via EEGLAB in MATLAB and freely available on GitHub. Given its performance and objectivity we recommend RELAX to improve artifact cleaning and consistency across ERP research.

Introducing RELAX: An automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and application to oscillations

OBJECTIVE

Electroencephalographic (EEG) data are often contaminated with non-neural artifacts which can confound experimental results. Current artifact cleaning approaches often require costly manual input. Our aim was to provide a fully automated EEG cleaning pipeline that addresses all artifact types and improves measurement of EEG outcomes METHODS: We developed RELAX (the Reduction of Electroencephalographic Artifacts). RELAX cleans continuous data using Multi-channel Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were compared using three datasets (N = 213, 60 and 23 respectively) against six commonly used pipelines across a range of artifact cleaning metrics, including measures of remaining blink and muscle activity, and the variance explained by experimental manipulations after cleaning.

RESULTS

RELAX with MWF and wICA_ICLabel showed amongst the best performance at cleaning blink and muscle artifacts while preserving neural signal. RELAX with wICA_ICLabel only may perform better at differentiating alpha oscillations between working memory conditions.

CONCLUSIONS

RELAX provides automated, objective and high-performing EEG cleaning, is easy to use, and freely available on GitHub.

SIGNIFICANCE

We recommend RELAX for data cleaning across EEG studies to reduce artifact confounds, improve outcome measurement and improve inter-study consistency.

EEG is better left alone

Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We compared optimized pipelines for preprocessing EEG data maximizing ERP significance using the leading open-source EEG software: EEGLAB, FieldTrip, MNE, and Brainstorm. Only one pipeline performed significantly better than high-pass filtering the data.

How robust is the relationship between neural processing speed and cognitive abilities?

Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.

Towards thoughtful planning of ERP studies: How participants, trials, and effect magnitude interact to influence statistical power across seven ERP components

In the field of EEG, researchers generally rely on rules of thumb, rather than a priori statistical calculations, when planning the number of trials to include in an ERP study. To aid in this practice, studies have tried to establish minimum numbers of trials required to reliably isolate ERPs. However, these guidelines do not necessarily apply across different study designs, as the reliability of an ERP waveform is not the same as the statistical power of a given experiment. Experiment parameters such as number of participants, trials, and effect magnitude interact to affect power in complex ways. Both under- and over-powered ERP studies represent a waste of time and resources that impedes the progress of the field. The current study fills this gap by subsampling real ERP data to estimate the relationship between experiment design parameters and statistical power. The simulations include seven commonly studied ERP components: the ERN, LRP, N170, MMN, P3, N2pc, and N400. In the first set of experiments, we determined the probability of obtaining a statistically significant ERP effect for each component. In the second and third set of experiments, we determined the probability of obtaining a statistically significant difference in ERP amplitude within and between groups for each component. Results indicate that the rules of thumb for ERP experiment design in the literature often lead to underpowered studies. Going forward, these results provide researchers with experiment design guidelines that are specific to the component under study, allowing for the design of sufficiently powered ERP studies.

The late positive potential during affective picture processing: Associations with daily life emotional functioning among adolescents with anxiety disorders

Pediatric anxiety disorders are characterized by potentiated threat responses and maladaptive emotion regulation (ER). The Late Positive Potential (LPP) is a neural index of heightened attention to emotional stimuli. Anxious individuals typically exhibit a larger LPP to unpleasant stimuli, but the LPP may also be blunted to unpleasant and pleasant stimuli for those with co-morbid depression. While a larger LPP is thought to reflect greater emotional reactivity, it is unknown to what extent variation in the LPP to laboratory stimuli corresponds to daily emotional functioning. We assessed the LPP in the laboratory in response to unpleasant, pleasant, and neutral images in combination with ecological momentary assessment of emotional reactivity and regulation in daily life among youth (9-14 years old; 55 % female) with anxiety disorders (ANX, N = 130) and no psychiatric diagnoses (ND, N = 47). We tested whether LPP amplitudes to unpleasant and pleasant stimuli (vs. neutral) are greater in ANX (vs. ND) youth and whether LPP amplitudes inversely correlate with co-morbid depression symptoms. We also examined associations between the LPP and daily life emotional functioning among ANX and ND youth. We found no group-by-valence effects on LPP amplitudes. Within ANX youth, higher depression symptoms were associated with smaller LPP amplitudes to unpleasant, but not pleasant, stimuli relative to neutral stimuli. Larger LPP amplitudes to emotional (relative to neutral) stimuli were correlated with use of specific ER strategies among ANX and ND youth but not emotional reactivity. While the LPP may reflect initial emotional reactivity to laboratory stimuli, it is associated with ER behaviors, and not emotional reactivity, in daily life.

Good scientific practice in EEG and MEG research: Progress and perspectives

Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.

Neural indices of performance monitoring are associated with daily emotional functioning in youth with anxiety disorders: An ERP and EMA study

Excessive monitoring of one's performance is a characteristic of anxiety disorders that has been linked to alterations in implicit emotion regulation (ER), including elevations in neural measures of performance monitoring (i.e., error- and correct-related negativity; ERN and CRN). Elevations in ERN and CRN amplitudes have been reported consistently in anxiety disorders, suggesting that an overactive performance monitoring system is linked to ER difficulties in anxiety. Yet, the relevance of these lab-based neural measures for day-to-day emotional functioning remains poorly understood. This study examined the degree to which ERN and CRN amplitudes are associated with measures of daily ER difficulties in youth with anxiety disorders. Youth (N = 100, Mage = 11.14, SDage = 1.46) completed a computerized flanker task assessing the ERN and CRN. They then completed a 5-day ecological momentary assessment (EMA) protocol assessing their daily ER (i.e., intensity of momentary and peak negative affect, intensity of worry, reliance on maladaptive ER strategies). Results showed that more negative mean CRN amplitudes were associated with higher levels of negative emotional reactivity and more intense worries. There were no significant associations between ERN amplitude and EMA measures. Furthermore, elevations in CRN were linked to more frequent use of maladaptive ER strategies (i.e., rumination, physiological reactivity, avoidance). Together, results indicate that among youth with anxiety, individual differences in CRN, but not ERN, amplitudes are related to daily ER difficulties. Findings highlight the clinical utility of a lab-based neural measure of ER, suggesting that the CRN, rather than the ERN, reflects individual ER differences in the context of daily life among youth with pediatric anxiety disorders. As such, the CRN might serve as an important dimensional index of a treatment target that can be tracked with a validated, multi-method measure.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Navigating the manyverse of skin conductance response quantification approaches - A direct comparison of trough-to-peak, baseline correction, and model-based approaches in Ledalab and PsPM

Raw data are typically required to be processed to be ready for statistical analyses, and processing pipelines are often characterized by substantial heterogeneity. Here, we applied seven different approaches (trough-to-peak scoring by two different raters, script-based baseline correction, Ledalab as well as four different models implemented in the software PsPM) to two fear conditioning data sets. Selection of the approaches included was guided by a systematic literature search by using fear conditioning research as a case example. Our approach can be viewed as a set of robustness analyses (i.e., same data subjected to different processing pipelines) aiming to investigate if and to what extent these different quantification approaches yield comparable results given the same data. To our knowledge, no formal framework for the evaluation of robustness analyses exists to date, but we may borrow some criteria from a framework suggested for the evaluation of "replicability" in general. Our results from seven different SCR quantification approaches applied to two data sets with different paradigms suggest that there may be no single approach that consistently yields larger effect sizes and could be universally considered "best." Yet, at least some of the approaches employed show consistent effect sizes within each data set indicating comparability. Finally, we highlight substantial heterogeneity also within most quantification approaches and discuss implications and potential remedies.

Open Science in human electrophysiology

Open science practices are gaining momentum in psychophysiological research, but at the nascent stage of this special issue in the International Journal of Psychophysiology there was no systematic collection of resources to support the adoption of open science practices specific to studies of human electrophysiology (EEG). The purpose of this special issue was to gather and provide resources that identify the idiosyncratic considerations and implications of open science practices specifically for studies of human EEG and event-related potentials (ERPs). Papers also show the importance of promoting good scientific practices in the application of open science principles to EEG and ERPs. This introduction to the special issue provides a roadmap for identifying the resources necessary to begin and improve the application of open science principles to EEG and ERP research. We are optimistic that open science practices will help increase the robustness, rigor, and replicability of EEG research and ultimately become the norm in studies of EEG and ERPs.

Understanding the Error in Psychopathology: Notable Intraindividual Differences in Neural Variability of Performance Monitoring

BACKGROUND

Abnormal performance monitoring is a possible transdiagnostic marker of psychopathology. Research on neural indices of performance monitoring, including the error-related negativity (ERN), typically examines group and interindividual (between-person) differences in mean/average scores. Intraindividual (within-person) variability in activity captures the capacity to dynamically adjust from moment to moment, and excessive variability appears maladaptive. Intraindividual variability in ERN represents a unique and largely unexamined dimension that might impact functioning. We tested whether psychopathology group differences (major depressive disorder [MDD], generalized anxiety disorder [GAD], obsessive-compulsive disorder [OCD]) or corresponding psychiatric symptoms account for intraindividual variability in single-trial ERN scores.

METHODS

High-density electroencephalogram (Electrical Geodesics, Inc.) was recorded during a semantic flanker task in 51 participants with MDD, 44 participants with GAD, 31 participants with OCD, and 56 psychiatrically-healthy participants. Mean ERN amplitude was scored 0-125ms following participant response across four fronto-central sites. Multilevel location-scale models were used to simultaneously examine interindividual and intraindividual differences in ERN.

RESULTS

Analyses indicated considerable intraindividual variability in ERN that was common across groups. However, we did not find strong evidence to support relationships between ERN and psychopathology groups or transdiagnostic symptoms.

CONCLUSIONS

These findings point to important methodological implications for studies of performance monitoring in healthy and clinical populations-the common assumption of fixed intraindividual variability (i.e., residual variance) may be inappropriate for ERN studies. Implementation of multilevel location-scale models in future research can leverage between-person differences in intraindividual variability in performance monitoring to gain a rich understanding of trial-to-trial performance monitoring dynamics.