| 引用本文: | 储银雪,陆智俊,裘旭益,吴奇.深度稀疏自编码网络识别飞行员疲劳状态[J].控制理论与应用,2019,36(6):850~857.[点击复制] |
| CHU Yin-xue,LU Zhi-jun,QIU Xu-yi,WU Qi.Using deep sparse auto-encoding network to identify pilots’ fatigue status[J].Control Theory and Technology,2019,36(6):850~857.[点击复制] |
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| 深度稀疏自编码网络识别飞行员疲劳状态 |
| Using deep sparse auto-encoding network to identify pilots’ fatigue status |
| 摘要点击 2625 全文点击 1060 投稿时间：2018-01-04 修订日期：2018-08-13 |
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| DOI编号 10.7641/CTA.2018.80011 |
| 2019,36(6):850-857 |
| 中文关键词 飞行员疲劳 脑电信号 深度稀疏自编码网络 Softmax分类器 |
| 英文关键词 pilots’ fatigue electroencephalogram signals deep sparse auto-encoding network Softmax classifier |
| 基金项目 国家自然科学基金(61671293,61473158,51705242), 上海浦江人才计划(15PJ1404300) |
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| 中文摘要 |
| 针对飞行员疲劳状态识别的复杂性和准确性，提出一种基于脑电信号的深度学习模型。首先对飞行员脑电信号进行滤波分解，提取delta波（0.5~4Hz）、theta波（5~8Hz）、alpha波（7~14Hz）、beta波（14~30Hz），提取基于脑电节律波的频域特征，作为识别模型的输入向量。其次，将一种基于深度稀疏自编码网络-Softmax模型用于飞行员疲劳状态识别，并与单层的稀疏自编码网络-softmax和传统方法PCA-Softmax模型识别结果进行比较。最后，实验结果显示，针对飞行员疲劳状态识别问题，所建立的学习模型具有很好的分类识别效果，具有较好的工程推广价值。 |
| 英文摘要 |
| Aiming at the complexity and accuracy of recognition of pilot’s fatigue states, a deep learning model based on electroencephalogram signals was proposed. Firstly, EEG signals were filtered and decomposed, and the delta wave (0.5 ~ 4Hz), theta wave (5 ~ 8Hz) ，alpha wave (7 ~ 14Hz) and beta wave (14 ~ 30Hz) were extracted and the frequency domain features of four rhythms were also extracted as the input vectors. Secondly, a deep sparse auto-encoding network-Softmax model was proposed to recognize pilots’ fatigue states. Its recognition results were compared with those of single layer sparse auto-encoding network-softmax and traditional PCA-Softmax model. Finally, the experimental results showed that the learning model has a good classification for the recognition of pilots'' fatigue states, and has a good value for engineering promotion. |