We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4-3 Hz), θ wave (4-7 Hz), α wave (8-13 Hz) and β wave (14-30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
针对飞行员疲劳状态识别的复杂性，本文基于脑电信号提出一种新的深度学习模型。一方面，利用小波包变换对飞行员脑电信号进行多尺度分解，提取了脑电信号的四个节律波段：δ 波（0.4～3 Hz）、θ 波（4～7 Hz）、α 波（8～13 Hz）和 β 波（14～30 Hz），将重组的波段信号作为纯净的脑电信号。另一方面，提出一种基于深度收缩自编码网络的飞行员疲劳状态识别模型，并与其他方法进行比较。实验结果显示，针对飞行员疲劳状态识别问题，所建立的新的深度学习模型具有很好的识别效果，识别准确率高达 91.67%。因此，研究基于深度收缩自编码网络的飞行员疲劳状态识别具有重要意义。.
Keywords: deep contractive auto-encoding network; electroencephalogram signals; pilots’ fatigue; wavelet packet transform.