Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios

ABSTRACT: Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and a logistic regression machine learning model, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dy

SUBMITTER: Cross Z 

PROVIDER: S-EPMC9519541 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature