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Assessing cognitive workloads of assembly workers during multi-task switching.

Bin Ren1, Qinyu Zhou1, Jiayu Chen2

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|September 29, 2023
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This summary is machine-generated.

Electroencephalography (EEG) can quantify mental workload during complex task switching. This brainwave analysis reveals task transitions increase cognitive load, highlighting the need for optimized task organization to reduce human errors.

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Area of Science:

  • Cognitive Neuroscience
  • Human-Computer Interaction
  • Human Factors Engineering

Background:

  • Complex assembly tasks involve multiple steps, demanding rapid judgment and action under pressure.
  • Human-related errors frequently occur during these tasks, particularly during task switching and action transitions.
  • Quantifying mental workload during these critical periods is essential for error reduction.

Purpose of the Study:

  • To implement an electroencephalography (EEG) approach for quantitative mental workload evaluation during task switching.
  • To analyze time-frequency and spectrum characteristics of EEG signals to reflect task demand.
  • To validate the EEG assessment by comparing it with the NASA Task Load Index (NASA-TLX).

Main Methods:

  • Utilized electroencephalography (EEG) to record brain activity during complex assembly tasks.
  • Applied time-frequency and spectrum analysis to EEG data, focusing on power spectral densities (PSDs).
  • Compared EEG findings with subjective evaluations from the National Aeronautics and Space Administration task load index (NASA-TLX).

Main Results:

  • Distinctive EEG signal patterns were observed in the gamma band (AF4 channel) and beta band (F3 channel) across different task stages.
  • An increase in the peak PSD envelope from 18 to 27 Hz was noted during the transition from idling to part selection.
  • These findings indicate elevated mental workload during the intervals between tasks.

Conclusions:

  • The task switching period is not a rest phase but involves significant cognitive demand.
  • EEG-based quantitative analysis effectively evaluates mental workload during task transitions.
  • Optimizing task organization is crucial to mitigate the increased mental workload and reduce errors.