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date: 03 August 2020

Abstract and Keywords

This article discusses the use of structured, multivariate Bayesian dynamic models in the analysis of experimental data involving large-scale electroencephalography (EEG) signals or time series generated on individuals subject to tasks inducing mental fatigue. It first provides an overview of the goals and challenges in the analysis of brain signals, using the EEG case as example, before describing the development and application of novel time-varying autoregressive and regime switching models, which incorporate relevant prior information via structured priors and fitted using novel, customized Bayesian computational methods. In the experiment, a subject was asked to perform simple arithmetic operations for a period of three hours. Prior to the experiment, the subject was confirmed to be alert. After the experiment ended, the subject was fatigued. The study demonstrates that Bayesian analysis is useful for real time detection of cognitive fatigue.

Keywords: dynamic models, electroencephalography (EEG) signals, autoregressive models, computational methods, Bayesian analysis, cognitive fatigue, regime switching models, brain signals

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