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    Assessing Task Mental Workload in Construction Projects: A Novel Electroencephalography Approach

    Source: Journal of Construction Engineering and Management:;2017:;Volume ( 143 ):;issue: 008
    Author:
    Jiayu Chen
    ,
    John E. Taylor
    ,
    Semra Comu
    DOI: 10.1061/(ASCE)CO.1943-7862.0001345
    Publisher: American Society of Civil Engineers
    Abstract: Inappropriate task allocation in construction activities results not only in lower productivity but also higher error rates and more work-related injuries. Without quantitative assessment of task demand, it is nearly impossible to achieve reasonable and flexible task allocation. However, it is extremely difficult to assess the mental/cognitive demand of construction tasks quantitatively and reliably. This paper introduces a novel electroencephalography (EEG) approach to estimate task mental workload based on the power spectral densities (PSDs) of major frequency bands. A preliminary experiment was designed and conducted to demonstrate the proposed assessment approach, and the National Aeronautics and Space Administration task load index (NASA-TLX) was adopted to validate the results. Four EEG channels were tested for 30 subjects in the preliminary experiment with three-way analysis of variance (ANOVA). Both the types of construction activities and frequency bands show statistically significant effects on PSDs of the EEG signals. Among all channels, the channel that is located at the left frontal parietal lobe (Fp1), especially the gamma band, showed the highest correlation with the mental load level. The final statistical results and the frequency bin model suggest that the estimation results are consistent with the NASA-TLX mental demand scores.
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      Assessing Task Mental Workload in Construction Projects: A Novel Electroencephalography Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4245681
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    contributor authorJiayu Chen
    contributor authorJohn E. Taylor
    contributor authorSemra Comu
    date accessioned2017-12-30T13:06:24Z
    date available2017-12-30T13:06:24Z
    date issued2017
    identifier other%28ASCE%29CO.1943-7862.0001345.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4245681
    description abstractInappropriate task allocation in construction activities results not only in lower productivity but also higher error rates and more work-related injuries. Without quantitative assessment of task demand, it is nearly impossible to achieve reasonable and flexible task allocation. However, it is extremely difficult to assess the mental/cognitive demand of construction tasks quantitatively and reliably. This paper introduces a novel electroencephalography (EEG) approach to estimate task mental workload based on the power spectral densities (PSDs) of major frequency bands. A preliminary experiment was designed and conducted to demonstrate the proposed assessment approach, and the National Aeronautics and Space Administration task load index (NASA-TLX) was adopted to validate the results. Four EEG channels were tested for 30 subjects in the preliminary experiment with three-way analysis of variance (ANOVA). Both the types of construction activities and frequency bands show statistically significant effects on PSDs of the EEG signals. Among all channels, the channel that is located at the left frontal parietal lobe (Fp1), especially the gamma band, showed the highest correlation with the mental load level. The final statistical results and the frequency bin model suggest that the estimation results are consistent with the NASA-TLX mental demand scores.
    publisherAmerican Society of Civil Engineers
    titleAssessing Task Mental Workload in Construction Projects: A Novel Electroencephalography Approach
    typeJournal Paper
    journal volume143
    journal issue8
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0001345
    page04017053
    treeJournal of Construction Engineering and Management:;2017:;Volume ( 143 ):;issue: 008
    contenttypeFulltext
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