用脑电波识别飞行员疲劳等级，准确率达88%
Decoding Fatigue Levels of Pilots Using EEG Signals with Hybrid Deep Neural Networks
- 融合卷积与LSTM的混合深度网络提取脑电信号特征
- 在10名飞行员实验中达到88.01%平均准确率
- 首次实现飞行员疲劳三等级分类，适合航空安全研究
飞行员心理状态检测至关重要，异常状态可能引发灾难性事故。本研究首次实现对飞行员疲劳程度的分类，包括正常、轻度疲劳和重度疲劳三种状态。采用包含五个卷积模块和一个长短期记忆模块的混合深度神经网络，从脑电图（EEG）信号中提取关键特征。实验在模拟飞行环境中进行，共有10名飞行员参与。相比四种传统模型，所提模型在疲劳等级分类上取得0.8801的平均准确率，优于其他模型至少0.0599。此外，该模型还能为受试者提供有效反馈。研究有望推动自主飞行与驾驶技术的发展，促进人工智能在航空领域的应用。
原文摘要 · Abstract (English)
The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels, specifically a normal state, low fatigue, and high fatigue. To the best of our knowledge, this is the first study to classify fatigue levels in pilots. Our approach employs the hybrid deep neural network comprising five convolutional blocks and one long short-term memory block to extract the significant features from electroencephalography signals. Ten pilots participated in the experiment, which was conducted in a simulated flight environment. Compared to four conventional models, our proposed model achieved a superior grand-average accuracy of 0.8801, outperforming other models by at least 0.0599 in classifying fatigue levels. In addition to successfully classifying fatigue levels, our model provided valuable feedback to subjects. Therefore, we anticipate that our study will make the significant contributions to the advancement of autonomous flight and driving technologies, leveraging artificial intelligence in the future.
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