EEG-Based Prediction of Reaction Time during Sleep Deprivation

Abstract

Prolonged wakefulness is known to adversely affect basic cognitive abilities such as object recognition and decision making. It affects the dynamics of neuronal networks in the brain and can even lead to hallucinations and epileptic seizures. In cognitive-intensive workplaces, there is a requirement to refine an objective method of quantifying the current level of cognitive capabilities, rather than relying on subjective self-reporting. In this study, we compiled EEG recordings from several sleep deprivation workshops held by the Israeli Air Force (IAF), done by flight cadets and unmanned aerial vehicle (UAV) operators. By extracting a wide range of EEG features and applying machine-learning techniques, we were able to accurately predict the reaction time of participants undergoing a simple psycho-motor vigilance task, which acted as a stand-in for basic cognitive functions. Furthermore, through the use of interpretability methods, we examined the importance of different EEG features and their contribution to changes in the behavioral metrics.

Sleep Deprivation, EEG Analysis, Machine Learning, Executive Functions, EEG Spectral Analysis, Cognitive Function, Critical Brain Dynamics, Signal Complexity

Accepted manuscripts

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