Spatiotemporal Dynamical Analysis of Brain Activity During Mental Fatigue Process

Mental fatigue is a common phenomenon with implicit and multidimensional properties. It brings dynamic changes in functional brain networks. However, the challenging problem of false positives appears when the connectivity is estimated by electroencephalography (EEG). In this article, we propose a novel framework based on spatial clustering to explore the sources of mental fatigue and functional activity changes caused by them. To suppress false positive observations, spatial clustering is implemented in brain networks. The nodes extracted by spatial clustering are registered back to the functional magnetic resonance imaging (fMRI) source space to determine the sources of mental fatigue. The wavelet entropy of EEG in a sliding window is calculated to find the temporal features of mental fatigue. Our experimental results show that the extracted nodes correspond to the fMRI sources across different subjects and different tasks. The entropy values on the extracted nodes demonstrate clearer staged decreasing changes (deactivation). Additionally, the synchronization among the extracted nodes is stronger than that among all the nodes in the deactivation stage. The initial time of the strong synchronized deactivation is consistent with the subjective fatigue time reported by the subjects themselves. It means the synchronization and deactivation correspond to the subjective feelings of fatigue. Therefore, this functional activity pattern may be caused by sources of mental fatigue. The proposed framework is useful for a wide range of prolonged functional imaging and fatigue detection studies.