| 面向飞行员疲劳状态监测的脑认知深度模型研究 |
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| 引用本文： | 裘旭益,仇峰,吴奇.面向飞行员疲劳状态监测的脑认知深度模型研究[J].航空电子技术,2020,51(4):13-19. |
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| 作者姓名： | 裘旭益 仇峰 吴奇 |
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| 作者单位： | 航空电子系统综合技术重点实验室,上海200233,上海交通大学,上海200240,上海交通大学,上海200240 |
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| 摘 要： | 通过分析飞行员脑电信号，构建了疲劳状态的彩色脑功率图，设计基于高斯牛顿在线变分方法的卷
积神经网络参数优化方法，形成一种新型脑功率图深度网络模型，有效实现脑功率图深度网络的模型分类识别
能力。相比于其它基于脑电信号的疲劳检测深度模型，疲劳状态认知的准确度提升了3%~5%。
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| 关 键 词： | 疲劳检测 航空安全 脑功率图 飞行员认知 脑电信号 |
| 收稿时间： | 2020/11/5 0:00:00 |
| 修稿时间： | 2020/11/17 0:00:00 |
Brain Cognitive Deep Model for Pilot Fatigue State Monitoring |
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| QIU Xu-yi,QIU Feng,WU Qi.Brain Cognitive Deep Model for Pilot Fatigue State Monitoring[J].Avionics Technology,2020,51(4):13-19. |
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| Authors: | QIU Xu-yi QIU Feng WU Qi |
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| Institution: | Key Laboratory of Avionics System Integration, Shanghai 200233, China;Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Jiao Tong University, Shanghai 200241, China |
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| Abstract: | By studying Electroencephalogram (EEG) signals of pilots, the brain power color map of fatigue state is constructed.
By designing a parameter optimization method of convolutional neural network using Gauss-Newton online
variational method, a new brain power map deep network model is formed, which effectively implementsthe model
classification and recognition ability of deep neural network for brain power map. Compared with other deep neural network
models based on EEG signals, the accuracy for fatigue detection is improved by 3%~5%. |
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| Keywords: | fatigue detection aviation safety brain power map pilot cognition EEG |
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