Literature DB >> 29061281

Electroencephalographic and peripheral temperature dynamics during a prolonged psychomotor vigilance task.

Enrique Molina1, Daniel Sanabria2, Tzyy-Ping Jung3, Ángel Correa4.   

Abstract

Attention lapses and fatigue are a main source of impaired performance that can lead to accidents. This study analyzed electroencephalographic (EEG) dynamics and body skin temperature as markers of attentional fluctuations in non-sleep deprived subjects during a 45min Psychomotor Vigilance Task (PVT). Independent Component Analysis and time-frequency analysis were used to evaluate the EEG data. Results showed a positive association between distal and distal-to-proximal gradient (DPG) temperatures and reaction time (RT); increments in EEG power in alpha-, theta- and beta-band frequencies in parieto-occipital, central-medial and frontal components, were associated with poor performance (slower RT) in the task. This generalized power increment fits with an increased activity in the default mode network, associated with attention lapses. This study highlights the potential use of the PVT as a tool to obtain individual physiological indices of vigilance and fatigue that could be applied to other vigilance tasks typically performed in occupational settings.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Attention fluctuations; Brain dynamics; Independent component analysis (ICA); Mental fatigue; Skin temperature; Sustained attention

Mesh:

Year:  2017        PMID: 29061281     DOI: 10.1016/j.aap.2017.10.014

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  5 in total

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Journal:  Sports (Basel)       Date:  2022-04-28

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Authors:  Alexandra Woolgar; Anina N Rich; Hamid Karimi-Rouzbahani
Journal:  Elife       Date:  2021-04-08       Impact factor: 8.140

3.  The role of chronotype in the interaction between the alerting and the executive control networks.

Authors:  Víctor Martínez-Pérez; Lucía B Palmero; Guillermo Campoy; Luis J Fuentes
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4.  Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.

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Journal:  Front Hum Neurosci       Date:  2022-07-14       Impact factor: 3.473

5.  Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG.

Authors:  Christina Yi Jin; Jelmer P Borst; Marieke K van Vugt
Journal:  Eur J Neurosci       Date:  2020-06-28       Impact factor: 3.386

  5 in total

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