Cognitive neurophysiology: Event-related potentials

Helfrich, Randolph F.; Knight, Robert T.

doi:10.1016/b978-0-444-64032-1.00036-9

Cited by 94 publications

(71 citation statements)

References 143 publications

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“…These neurobiological correlates can be investigated effectively with electroencephalography (EEG). Indeed, EEG represents a good and appropriate technique to analyze the neurophysiology of both normal and psychotic experience and behavior, based on an integrative, complex and in-vivo model of the brain (29)(30)(31)(32)(33). In addition, this technique is non-invasive, and, in comparison to other imaging techniques, such as functional magnetic resonance imaging (fMRI), EEG has the advantages that it is more flexible in study design, has lower costs and it exhibits a superior temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the third category is constituted by event-related potentials (ERPs), which reflect the neuronal response following a specific sensory, cognitive, or motor event ( 32 , 41 ). These EEG indices can manifest as positive and negative voltage deflections, waves, or components with a precise temporal correlation to the onset of a specific event ( 42 ).…”

Section: Introductionmentioning

confidence: 99%

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EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

et al. 2021

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Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia.Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis.Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands.Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

et al. 2021

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“…In this section, we provide guidelines on how to transparently document the planned analytic choices in a preregistration of a prototypical ERP study. We focus on ERPs because of their widespread use in cognitive and clinical research (Hajcak et al, 2019;Helfrich and Knight, 2019) , although most of these recommendations can still be useful when using other signal processing techniques (e.g., ERP and time-frequency analyses have many preprocessing steps in common). Furthermore, we only include sections that would decrease researchers' flexibility during signal preprocessing and statistical analysis.…”

Section: Recommendations For Preregistration Of Erp Researchmentioning

confidence: 99%

Making ERP Research More Transparent: Guidelines for Preregistration

Paul¹,

Govaart²,

Schettino³

2020

Preprint

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Confirmation bias and pressure to publish may prompt the (unconscious) exploration of various methodological options and reporting only the ones that lead to a (statistically) significant outcome. This undisclosed analytic flexibility increases false positive results and inflates effect sizes, ultimately creating a skewed representation of knowledge. This issue is particularly relevant in EEG research, where a myriad of preprocessing and analysis pipelines can be used to extract information from complex multidimensional data. One solution to limit undisclosed analytic flexibility is preregistration: researchers write a time-stamped, publicly accessible research plan with hypotheses, data collection plan, and intended preprocessing and statistical analyses before the start of a research project. In this manuscript, we present an overview of the problems associated with undisclosed analytic flexibility (particularly in human neuroimaging and electrophysiology), discuss why and how EEG researchers would benefit from adopting preregistration, provide guidelines and examples on how to preregister data preprocessing and analysis steps in typical ERP studies, and conclude by addressing common rebuttals as well as clarifying possibilities and limitations of this open science practice.

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“…This neural signal is generally chosen from a variety of well-studied non-invasive electroencephalography (EEG) and magnetoencephalography (MEG) signals (Lawhern et al, 2018). The detection of ERP onsets can be used to identify the pictures of interest in the sequence (Helfrich and Knight, 2019). As a result, the RSVP paradigm has been used in multiple BCI applications, e.g., picture identification, screen spellers, and other applications that require identifying target stimulus at high speed.…”

Section: Introductionmentioning

confidence: 99%

Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data

Zhang

Qiu

Wang

et al. 2021

Front. Comput. Neurosci.

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Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain–computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm.Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target.Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from others, and the separation significantly increased the ERP detection scores in the EEGNet classifier. Moreover, the visualization of the new method suggested the different underling of SVM and EEGNet classifiers in ERP detection of the RSVP experiment.Conclusion: In the RSVP experiment, the near-non-target samples contain separable ERP activity. The ERP detection scores can be increased using classifiers of the EEGNet model, by separating the non-target into near- and far-targets based on their delay against targets.

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Cognitive neurophysiology: Event-related potentials

Cited by 94 publications

References 143 publications

EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

Making ERP Research More Transparent: Guidelines for Preregistration

Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data

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