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首页> 美国卫生研究院文献>Frontiers in Physiology >Dual Frequency Head Maps: A New Method for Indexing Mental Workload Continuously during Execution of Cognitive Tasks
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Dual Frequency Head Maps: A New Method for Indexing Mental Workload Continuously during Execution of Cognitive Tasks

机译：双频头部图：一种在执行认知任务过程中连续索引精神工作量的新方法

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One goal of advanced information and communication technology is to simplify work. However, there is growing consensus regarding the negative consequences of inappropriate workload on employee's health and the safety of persons. In order to develop a method for continuous mental workload monitoring, we implemented a task battery consisting of cognitive tasks with diverse levels of complexity and difficulty. We conducted experiments and registered the electroencephalogram (EEG), performance data, and the NASA-TLX questionnaire from 54 people. Analysis of the EEG spectra demonstrates an increase of the frontal theta band power and a decrease of the parietal alpha band power, both under increasing task difficulty level. Based on these findings we implemented a new method for monitoring mental workload, the so-called Dual Frequency Head Maps (DFHM) that are classified by support vectors machines (SVMs) in three different workload levels. The results are in accordance with the expected difficulty levels arising from the requirements of the tasks on the executive functions. Furthermore, this article includes an empirical validation of the new method on a secondary subset with new subjects and one additional new task without any adjustment of the classifiers. Hence, the main advantage of the proposed method compared with the existing solutions is that it provides an automatic, continuous classification of the mental workload state without any need for retraining the classifier—neither for new subjects nor for new tasks. The continuous workload monitoring can help ensure good working conditions, maintain a good level of performance, and simultaneously preserve a good state of health.
机译：先进的信息和通信技术的目标之一就是简化工作。但是，关于不适当的工作量对员工的健康和人员安全的负面影响，人们越来越达成共识。为了开发一种连续的心理工作量监视方法，我们实施了一个任务组，该任务组由具有各种复杂性和难度的认知任务组成。我们进行了实验，并记录了来自54个人的脑电图（EEG），性能数据和NASA-TLX问卷。对脑电图谱的分析表明，在增加任务难度的情况下，额叶θ带功率增加，顶叶α带功率减小。基于这些发现，我们实施了一种监视心理工作量的新方法，即所谓的双频头标图（DFHM），该方法通过支持向量机（SVM）在三个不同的工作量级别上进行分类。结果符合执行职能上的任务要求所产生的预期难度级别。此外，本文还对新方法的经验方法进行了验证，该方法适用于具有新主题的次要子集，并且无需对分类器进行任何调整即可完成一项新任务。因此，与现有解决方案相比，所提出方法的主要优势在于，它无需重新训练分类器即可提供精神工作量状态的自动，连续分类，而无需为新主题或新任务进行分类。连续的工作负载监视可以帮助确保良好的工作条件，保持良好的性能水平，并同时保持良好的健康状态。

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