Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best predict whether an individual is confronted with a situation requiring the inhibition of a response. This is particularly the case when considering the complex spatio-temporal nature of neural processes captured by EEG data. In the current study, we ask whether independent spatial activity profiles in the EEG data are useful to predict whether an individual is confronted with a situation requiring response inhibition. We combine independent component analysis (ICA) with explainable artificial intelligence approaches (EEG-based deep learning) using data from a Go/Nogo task (N = 255 participants). We show that there are four dissociable spatial activity profiles important to classify Go and Nogo trials as revealed by deep learning. Of note, for all of these four independent activity profiles, neural activity in the time period between 300 and 550 ms after stimulus presentation was most informative. Source localization analyses further revealed regions in the pre-central gyrus (BA6), the middle frontal gyrus (BA10), the inferior frontal gyrus (BA46), and the insular cortex (BA13) were associated with the isolated spatial activity profiles. The data suggest concomitant processes being reflected in the identified time window. This has implications for the ongoing debate on the functional significance of event-related potential correlates of inhibitory control.

Details

OriginalspracheEnglisch
Aufsatznummere14328
FachzeitschriftPsychophysiology
Jahrgang60
Ausgabenummer10
Frühes Online-Datum12 Mai 2023
PublikationsstatusVeröffentlicht - Okt. 2023
Peer-Review-StatusJa

Externe IDs

PubMed 37171032
ORCID /0000-0002-2989-9561/work/151436211
ORCID /0000-0002-9069-7803/work/151437478

Schlagworte

Schlagwörter

  • EEG, deep learning, independent component analysis, response inhibition, Evoked Potentials/physiology, Electroencephalography/methods, Humans, Artificial Intelligence, Executive Function/physiology, Deep Learning