论文标题
使用脑电图信号的混合深神经网络对干扰水平进行分类
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals
论文作者
论文摘要
已经开发出非侵入性脑部计算机界面技术来检测性能高的人类精神状态。检测飞行员的精神状态尤其重要，因为他们的异常精神状态可能导致灾难性事故。在这项研究中，我们介绍了通过应用深度学习方法分类分散注意力水平（即正常状态，低干扰和分心）的可行性。据我们所知，这项研究是在飞行环境下进行分心水平进行分类的首次尝试。我们提出了一个模型，以分类干扰水平。共有十名飞行员在模拟的飞行环境中进行了实验。对于所有受试者的分心水平进行分类的分类水平为0.8437。因此，我们认为，它将在未来基于人工智能技术的基于人工智能技术的自动驾驶或飞行中做出重大贡献。
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.
