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Abstract：
Complex assembly tasks with multiple manual operations and steps often require rapid judgment and action under time pressure and cause most human-related errors. The task switching and action transitions are major sources of these errors. This study intends to implement an electroencephalography (EEG) approach to quantitatively evaluate the mental workload during task switching and transition. The time-frequency and spectrum analysis were utilized to compute and reflect the task demand between the intervals of individual tasks. This study developed an experiment to validate the proposed assessment approach and benchmark the results with the National Aeronautics and Space Administration task load index (NASA-TLX) subjective evaluation scale analysis. The results show that the average value of the power spectral densities (PSDs) of the gamma band signal of the AF4 channel and the beta band signal of Channel F3 show distinctive signal patterns among task stages and intervals. During the interval between the idling stage and the part selection stage, the peak of the PSD envelope increased from 18 to 27 Hz, suggesting advanced cognition increases the mental workload of the interval between different tasks. Therefore, the task switching period cannot be regarded as rest and need to be optimized with better task organization.
摘要：
具有多个手动操作和步骤的复杂装配任务通常需要在时间压力下进行快速判断和动作，并导致大多数与人类相关的错误。任务切换和动作转换是这些错误的主要来源。本研究旨在实施脑电图（EEG）方法，以定量评估任务切换和过渡期间的心理工作量。时频和频谱分析用于计算和反映各个任务间隔之间的任务需求。本研究开发了一项实验，以验证所提出的评估方法，并通过美国国家航空航天局任务负荷指数（NASA-TLX）主观评估量表分析对结果进行基准测试。结果表明，AF4通道的伽马带信号和通道F3的β带信号的功率谱密度（PSD）的平均值在任务阶段和间隔之间显示出独特的信号模式。在空转阶段和零件选择阶段之间的间隔期间，PSD包络的峰值从18Hz增加到27Hz，提示高级认知增加了不同任务之间间隔的心理工作量。因此,任务切换周期不能视为休息,需要更好的任务组织进行优化。
