Are electrophysiological correlates of response inhibition linked to impulsivity and compulsivity? A machine‐learning analysis of a Go/Nogo task

Dück, Kerstin; Overmeyer, Rebecca; Mohr, Holger; Endraß, Tanja

doi:10.1111/psyp.14310

Psychophysiology

2023

DOI: 10.1111/psyp.14310

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Are electrophysiological correlates of response inhibition linked to impulsivity and compulsivity? A machine‐learning analysis of a Go/Nogo task

Kerstin Dück

Rebecca Overmeyer

Holger Mohr

et al.

Abstract: Heightened impulsivity and compulsivity are often found in association with both dysfunctional everyday behavior and with psychopathology. Impulsivity and compulsivity are also linked to alterations in behavioral response inhibition and its electrophysiological correlates. However, they are rarely examined jointly and their effect outside of clinical samples is still disputed. This study

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Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task

Bryniarska,

Ramos,

Fernández

2024

Entropy

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Machine learning (ML) methods are increasingly being applied to analyze biological signals. For example, ML methods have been successfully applied to the human electroencephalogram (EEG) to classify neural signals as pathological or non-pathological and to predict working memory performance in healthy and psychiatric patients. ML approaches can quickly process large volumes of data to reveal patterns that may be missed by humans. This study investigated the accuracy of ML methods at classifying the brain’s electrical activity to cognitive events, i.e., event-related brain potentials (ERPs). ERPs are extracted from the ongoing EEG and represent electrical potentials in response to specific events. ERPs were evoked during a visual Go/NoGo task. The Go/NoGo task requires a button press on Go trials and response withholding on NoGo trials. NoGo trials elicit neural activity associated with inhibitory control processes. We compared the accuracy of six ML algorithms at classifying the ERPs associated with each trial type. The raw electrical signals were fed to all ML algorithms to build predictive models. The same raw data were then truncated in length and fitted to multiple dynamic state space models of order nx using a continuous-time subspace-based system identification algorithm. The 4nx numerator and denominator parameters of the transfer function of the state space model were then used as substitutes for the data. Dimensionality reduction simplifies classification, reduces noise, and may ultimately improve the predictive power of ML models. Our findings revealed that all ML methods correctly classified the electrical signal associated with each trial type with a high degree of accuracy, and accuracy remained high after parameterization was applied. We discuss the models and the usefulness of the parameterization.

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Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task

Bryniarska,

Ramos,

Fernández

2024

Entropy

View full text Add to dashboard Cite

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Are electrophysiological correlates of response inhibition linked to impulsivity and compulsivity? A machine‐learning analysis of a Go/Nogo task

Cited by 1 publication

References 143 publications

Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task

Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task

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