Abstract
Mental fatigue was associated with increased power in frontal theta (θ) and parietal alpha (α) EEG rhythms. A statistical classifier can use these effects to model EEG-fatigue relationships accurately. Participants (n = 22) solved math problems on a computer until either they felt exhausted or 3 h had elapsed. Pre- and post-task mood scales showed that fatigue increased and energy decreased. Mean response times rose from 6.7 s to 7.9 s but accuracy did not change significantly. Mean pow- er spectral densities or PSDs of θ and α bands rose by 29% and 44%, respectively. A kernel partial least squares classifier trained to classify PSD coefficients (1 - 18 Hz) of single 13-s EEG segments from alert or fatigued task periods was 91% to 100% accurate. For EEG segments from other task periods, the classifier outputs tracked the time course of the development of mental fatigue. By this measure, most subjects became substantially fatigued after 60 min of task performance. However, the trajectories of individual classifier outputs showed that EEG signs of developing fa- tigue were present in all subjects after 15 - 30 minutes of task performance. The results show that EEG can track the development of mental fatigue over time with accurate updates on a time scale a short as 13 seconds. In addition, the results agree with the notion that growing mental fatigue produces a shift away from executive and attention networks to default mode and is accompanied by a shift in alpha frequency to the lower alpha band.
Highlights
The purpose of this study was two-fold: a) to identify the features of the electroencephalogram or EEG that change in an orderly and reliable manner with the development of mental fatigue; and b) to construct a practical system that can use EEG features to accurately estimate the instantaneous degree of mental fatigue over a performance period of up to three hours
While many studies have considered EEG correlates of general fatigue, drowsiness and sleepiness, our study focused on inducing mental fatigue through sustained performance of a cognitive task for up to three hours, while minimizing other effects
We find the necessary and sufficient set of basis vectors using algorithms that extract each vector sequentially, in descending rank of its covariance with an output measure, which in this case is a distinction between alertness and fatigue
Summary
The purpose of this study was two-fold: a) to identify the features of the electroencephalogram or EEG that change in an orderly and reliable manner with the development of mental fatigue; and b) to construct a practical system that can use EEG features to accurately estimate the instantaneous degree of mental fatigue over a performance period of up to three hours. We adopted a task and a set of procedures for inducing mental fatigue in the participants of the study. We selected an EEG measurement system that is practical enough to use in operational settings and sensitive enough to detect changes in EEG features with the development of fatigue. We adapted an algorithm for estimation of mental fatigue from EEG features that is robust in the face of signal corruption from noise or sensor loss and accurate enough to classify states of mental fatigue in individual human subjects with a few seconds of EEG recordings
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