A1 Journal article (refereed)
Spatio-temporal Dynamical Analysis of Brain Activity during Mental Fatigue Process (2021)

Zhang, C., Sun, L., Cong, F., & Ristaniemi, T. (2021). Spatio-temporal Dynamical Analysis of Brain Activity during Mental Fatigue Process. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 593-606. https://doi.org/10.1109/TCDS.2020.2976610

JYU authors or editors

Publication details

All authors or editors: Zhang, Chi; Sun, Lina; Cong, Fengyi; Ristaniemi, Tapani

Journal or series: IEEE Transactions on Cognitive and Developmental Systems

ISSN: 2379-8920

eISSN: 2379-8939

Publication year: 2021

Volume: 13

Issue number: 3

Pages range: 593-606

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/TCDS.2020.2976610

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/68002

Abstract

Mental fatigue is a common phenomenon with implicit and multidimensional properties. It brings dynamic changes of functional brain networks. However, the challenging problem of false positives appears when the connectivity is estimated by Electroencephalography (EEG). In this paper, 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 the false positive observations, spatial clustering is implemented in brain networks. The nodes extracted by spatial clustering are registered back to functional magnetic resonance imaging (fMRI) source space to determined 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 corresponds to the subjective feelings of fatigue. Therefore, this functional activity pattern may be caused by the sources of mental fatigue. The proposed framework is useful for a wide range of prolonged functional imaging and fatigue detection studies.

Keywords: fatigue (biological phenomena); neural networks (biology); EEG; functional magnetic resonance imaging; imaging; signal analysis; signal processing; cluster analysis

Free keywords: mental fatigue; EEG; spatiotemporal imaging; functional connectivity; spatial clustering

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 1