The reliability and psychometric structure of Multi-Scale Entropy measured from EEG signals at rest and during face and object recognition tasks

Background: Multi-Scale Entropy (MSE) is a widely used marker of Brain Signal Complexity (BSC) at multiple temporal scales. Methodological improvement: There is no systematic research addressing the psychometric quality and reliability of MSE. It is unknown how recording conditions of EEG signals affect individual differences in MSE. These gaps can be addressed by means of Structural Equation Modeling (SEM). Results: Based on a large sample of 210 young adults, we estimated measurement models for MSE derived from multiple epochs of EEG signal measured during resting state conditions with closed and open eyes, and during a visual task with multiple experimental manipulations. Factor reliability estimates, quantified by the McDonald's ω coefficient, are high at lower and acceptable at higher time scales. Above individual differences in signal entropy observed across all recording conditions, persons specifically differ with respect to their BSC in open eyes resting state condition as compared with closed eyes state, and in task processing state MSE as compared with resting state. Comparison with existing methods: By means of SEM, we decomposed individual differences in BSC into different factors depending on the recording condition of EEG signals. This goes beyond existing methods that aim at estimating average MSE differences across recording conditions, but do not address whether individual differences are additionally affected by the type of EEG recording condition. Conclusion: Eyes closed and open and task conditions strongly influence individual differences in MSE. We provide recommendations for future studies aiming to address BSC using MSE as a neural marker of cognitive abilities.