Linear mixed-effect models for correlated response to process electroencephalogram recordings

• Published in: Cognitive neurodynamics Vol. 18; no. 3; pp. 1197 - 1207
• Main Authors: Meinardi, Vanesa B., López, Juan M. Díaz, Fajreldines, Hugo Diaz, Boyallian, Carina, Balzarini, Monica
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Cognitive Psychology; Complexity; Computer Science; Correlation; Data acquisition; EEG; Electroencephalography; Entropy; Entropy (Information theory); Hyperventilation; Hypotheses; Hypothesis testing; Information theory; Methods; Neurosciences; Permutations; Research Article; Sleep and wakefulness; Statistical analysis; Wakefulness; Mixed linear models; Permutation entropy; Electroencephalography; Correlate responses; Lempel-Ziv complexity
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-023-09984-6

A data set of clinical studies of electroencephalogram recordings (EEG) following data acquisition protocols in control individuals (Eyes Closed Wakefulness - Eyes Open Wakefulness, Hyperventilation, and Optostimulation) are quantified with information theory metrics, namely permutation Shanon entropy and permutation Lempel Ziv complexity, to identify functional changes. This work implement Linear mixed-effects models (LMEMs) for confirmatory hypothesis testing. The results show that EEGs have high variability for both metrics and there is a positive correlation between them. The mean of permutation Lempel-Ziv complexity and permutation Shanon entropy used simultaneously for each of the four states are distinguishable from each other. However, used separately, the differences between permutation Lempel-Ziv complexity or permutation Shanon entropy of some states were not statistically significant. This shows that the joint use of both metrics provides more information than the separate use of each of them. Despite their wide use in medicine, LMEMs have not been commonly applied to simultaneously model metrics that quantify EEG signals. Modeling EEGs using a model that characterizes more than one response variable and their possible correlations represents a new way of analyzing EEG data in neuroscience.