EEG signal analysis for human workload classification

C. Ling, H. Goins, A. Ntuen, R. Li
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引用次数: 11

Abstract

This paper provides the results of determining the state of a human pilot operator by using electroencephalograph (EEG) data. The state of a human operator is used to represent the mental (cognitive) workload experienced during task execution. This study used EEG data gathered from a crew-simulation laboratory environment. By using EEG data from twelve subjects encountering six simulated pilot workload levels, we set up a neural network to obtain an overall mean classification accuracy of over 80%. A comparison between the conventional backpropagation method and the resilient backpropagation method also shows that a significant reduction in training time can be achieved.
脑电信号分析用于人类工作负荷分类
本文给出了利用脑电图数据判断驾驶员状态的结果。人类操作员的状态用于表示在任务执行过程中所经历的精神(认知)工作量。这项研究使用了从模拟实验室环境中收集的脑电图数据。利用12名受试者在6个模拟飞行员工作负荷水平下的脑电数据,建立了一个神经网络,总体平均分类准确率超过80%。将传统的反向传播方法与弹性反向传播方法进行比较,也可以显著减少训练时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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