Non-invasive brain-computer interface technology has been developed for
detecting human mental states with high performances. Detection of the pilots'
mental states is particularly critical because their abnormal mental states
could cause catastrophic accidents. In this study, we presented the feasibility
of classifying distraction levels (namely, normal state, low distraction, and
high distraction) by applying the deep learning method. To the best of our
knowledge, this study is the first attempt to classify distraction levels under
a flight environment. We proposed a model for classifying distraction levels. A
total of ten pilots conducted the experiment in a simulated flight environment.
The grand-average accuracy was 0.8437 for classifying distraction levels across
all subjects. Hence, we believe that it will contribute significantly to
autonomous driving or flight based on artificial intelligence technology in the
future.

本研究使用深度学习方法，探索在飞行环境下分类不同注意力水平的可行性，结果表明可以对不同注意力水平进行有效的分类，对未来的人工智能技术驱动下的自主飞行或驾驶具有重要贡献。