A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatZhang, Chi; Sun, Lina; Cong, Fengyi; Ristaniemi, Tapani

Lehti tai sarjaIEEE Transactions on Cognitive and Developmental Systems

ISSN2379-8920

eISSN2379-8939

Julkaisuvuosi2021

Volyymi13

Lehden numero3

Artikkelin sivunumerot593-606

KustantajaIEEE

JulkaisumaaYhdysvallat (USA)

Julkaisun kielienglanti

DOIhttps://doi.org/10.1109/TCDS.2020.2976610

Julkaisun avoin saatavuusEi avoin

Julkaisukanavan avoin saatavuus

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/68002


Tiivistelmä

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.


YSO-asiasanatväsymyshermoverkot (biologia)EEGtoiminnallinen magneettikuvauskuvantaminensignaalianalyysisignaalinkäsittelyklusterianalyysi

Vapaat asiasanathenkinen väsymys; aivosähkökäyrä; elektroenkefalografia; spatiotemporaalinen analyysi


Liittyvät organisaatiot


OKM-raportointiKyllä

Raportointivuosi2021

JUFO-taso1


Viimeisin päivitys 2024-03-04 klo 21:25