基于Gamma深度信念网络的飞行员脑疲劳状态识别

doi:10.3969/j.issn.0372-2112.2020.06.003

电子学报 ›› 2020, Vol. 48 ›› Issue (6): 1062-1070.DOI: 10.3969/j.issn.0372-2112.2020.06.003

基于Gamma深度信念网络的飞行员脑疲劳状态识别

罗映雪¹, 贾博², 裘旭益³, 邓平煜³, 任和⁴, 吴奇^1,3

1. 上海交通大学电子信息与电气工程学院自动化系, 上海 200240;
2. 中国东方航空技术应用研发有限公司, 上海 201700;
3. 中国航空无线电电子研究所, 上海 200233;
4. 中国商飞上海飞机客户服务有限公司, 上海 200241

收稿日期:2019-06-13 修回日期:2019-12-06 出版日期:2020-06-25
- 通讯作者:
- 吴奇
- 作者简介:
- 罗映雪 女,1996年出生,湖北洪湖人,上海交通大学控制工程专业硕士研究生,主要研究方向为飞行员工作负荷评价. E-mail:dyljzxl@sjtu.edu.cn
  贾博 男,1988年出生,甘肃兰州人,东航技术应用研发中心有限公司工程师,主要研究方向为人机交互,智能飞行.
  裘旭益 男,1983年出生,浙江慈溪人,中国航空无线电电子研究所高级工程师,上海交通大学博士研究生,主要研究方向为智能作战与人机交互.
  邓平煜 男,1979年出生,甘肃张掖人,中国航空无线电电子研究所总工程师,研究员,主要研究方向为航电系统架构与人机交互.
  任和 男,1967年出生,澳洲人,中国商飞上海飞机客户服务有限公司副总工程师,国家千人计划教授,主要研究方向为飞行数据管理与人机交互.
- 基金资助:
- 国家自然科学基金项目 (No.61671293），国家自然科学民航联合基金项目 (No.U1933125）

Pilots' Brain Fatigue State Inference Based on Gamma Deep Belief Network

LUO Ying-xue¹, JIA Bo², QIU Xu-yi³, DENG Ping-yu³, REN He⁴, WU Qi^1,3

1. Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;
2. China Eastern Airlines Technology Application R&D Center Co., Ltd., Shanghai, Shanghai 201700, China;
3. China National Aeronautical Radio Electronics Research Institute, Shanghai 200233, China;
4. COMAC Shanghai Aircraft Customer Service Co., Ltd., Shanghai 200241, China

Received:2019-06-13 Revised:2019-12-06 Online:2020-06-25 Published:2020-06-25

摘要/Abstract

摘要： 飞行员疲劳状态识别面临两个重要问题，如何提取表征疲劳的特征以及如何对疲劳特征建模学习.首先提取脑电信号节律波，计算基于仿射伪平滑Wigner-Ville分布的瞬时频域信息，构建疲劳状态指标.其次，基于脑电信号各通道的周期性变化提出Gamma深度信念网络的疲劳状态分类算法，与采用卷积与池化运算的学习网络不同，Gamma深度信念网络没有将图像或信号按尺度分割，但在底部的隐藏层已经可以有效地学习特定区域的特征，且当层数增加时，可有效提取特征的区域增多，学习到的特征更为一般化.然后改进用于训练深度信念网络的Gibbs采样算法，提出向上向下Gibbs采样以推断网络参数.最后，实验结果显示，本文的Gamma深度信念网络在识别准确率、稳定性、迭代用时等方面均达到了令人满意的效果.

关键词: 脑电信号, 飞行员疲劳, 疲劳识别, Gamma深度信念网络

Abstract: Pilots' fatigue state recognition faces two important issues:how to extract the characteristics that characterize fatigue and how to model fatigue characteristics.Firstly,the EEG(ElectroEncephaloGram) signal is extracted,and the instantaneous frequency domain information based on the affine pseudo-smooth Wigner-Ville distribution is calculated to construct the fatigue state index.Secondly,based on the periodic changes of each channel of EEG signals,the fatigue state classification algorithm of Gamma deep belief network is proposed.Unlike other learning network using convolution and pooling,the proposed network does not split the image or signal,but the hidden layer at the bottom can effectively learn the features of a specific region,and when the number of layers increases,the number of features increases and the features are more general.The Gibbs sampling algorithm for training the deep belief network is improved.The up-down Gibbs sampling is proposed to infer the network parameters.Finally,the experimental results show that the Gamma deep belief network in this paper has achieved satisfactory results in terms of recognition accuracy,stability and iteration time.

Key words: EEG signals, pilots' fatigue, fatigue recognition, Gamma deep belief network

中图分类号:

TP391.4

罗映雪, 贾博, 裘旭益, 邓平煜, 任和, 吴奇. 基于Gamma深度信念网络的飞行员脑疲劳状态识别[J]. 电子学报, 2020, 48(6): 1062-1070.

LUO Ying-xue, JIA Bo, QIU Xu-yi, DENG Ping-yu, REN He, WU Qi. Pilots' Brain Fatigue State Inference Based on Gamma Deep Belief Network[J]. Acta Electronica Sinica, 2020, 48(6): 1062-1070.

参考文献

[1] Maarten A S,Theo F,Monicque M.Effects of mental fatigue on attention:An ERP study[J].Cognitive Brain Research,2005,25(1):107-116.
[2] Moroney W,Biers D W,Eggemeier F T,et al.A comparison of two scoring procedures with the NASA task load index in a simulated flight task[A].Proceedings of the IEEE 1992 National Aerospace and Electronics Conference[C].Dayton,OH,USA:IEEE,1992.734-740.
[3] Kaida K,Takahashi M,Akerstedt T,et al.Validation of the Karolinska sleepiness scale against performance and EEG variables[J].Clinical Neurophysiology,2006,117(7):1574-1581.
[4] Hong J W,Sun W L,Jean-Philippe P.Real time eye tracking interface for visual monitoring of radar controllers[A].AIAA Modeling and Simulation Technologies Conference[C].Grapevine,Texas,USA:AIAA,2017.1317.
[5] Nijholt A,Tan D.Brain-computer interfacing for intelligent systems[J].IEEE Intelligent Systems,2008,23(3):72-79.
[6] Craig A,Tran Y,Wijesuriya N,et al.Regional brain wave activity changes associated with fatigue[J].Psychophysiology,2012,49(4):574-582.
[7] Lal S K L,Craig A,Boord P,et al.Development of an algorithm for an EEG-based driver fatigue countermeasure[J].Journal of Safety Research,2003,34(3):321-328.
[8] Azarnoosh M,Nasrabadi A M,Mohammadi M R,et al.Investigation of mental fatigue through EEG signal processing based on nonlinear analysis:Symbolic dynamics[J].Chaos Solitons Fractals,2011,44(12):1054-1062.
[9] Lal S K L,Craig A.A critical review of the psychophysiology of driver fatigue[J].Biological Psychology,2001,55(3):173-194.
[10] C Papadelis,et al.Indicators of sleepiness in an ambulatory EEG study of night driving[A].International Conference of the IEEE Engineering in Medicine and Biology Society[C].New York:IEEE,2006.6201-6204.
[11] Jap B T,Lal S,Fischer P,et al.Using EEG spectral components to assess algorithms for detecting fatigue[J].Expert Systems with Applications,2009,36(2):2352-2359.
[12] Cai H,Sha X,Han X,et al.Pervasive EEG diagnosis of depression using deep belief network with three-electrodes EEG collector[A].IEEE International Conference on Bioinformatics and Biomedicine[C].Shenzhen:IEEE,2016.1239-1246.
[13] Chen J X,Zhang P W,Mao Z J,et al.Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks[J].IEEE Access,2019,7:44317-44328.
[14] 孙曜,文成林,韦巍.基于脑电和眼电的运动想象多尺度识别方法研究[J].电子学报,2018,46(03):714-720. Sun Yao,Wen Chenglin,Wei Wei.Research on EEG and EOG based multiscale recognization method of motor imagery[J].Acta Electronica Sinica,2018,46(03):714-720.(in Chinese)
[15] Saul L K,Jaakkola T,Jordan M I.Mean field theory for sigmoid belief networks[J].Journal of Artificial Intelligence Research,1996,4(1):61-76.
[16] Hinton G E,Osindero S,The Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554.
[17] Zhou M,Hannah L,Dunson D,et al.Beta-negative binomial process and poisson factor analysis[A].International Conference on Artificial Intelligence and Statistics[C].La Palma:IEEE,2012.1462-1471.
[18] Dyk D A V,Meng X L.The art of data augmentation[J].Journal of Computational & Graphical Statistics,2001,10(1):1-50.
[19] Blackwell,Macqueen J.Ferguson distributions via Polya urn schemes[J].The Annals of Statistics,1973,1(2):353-355.
[20] Juang C F.A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms[J].IEEE Transactions on Fuzzy Systems,2002,10(2):155-170.
[21] Yilmaz S,Oysal Y.Fuzzy wavelet neural network models for prediction and identification of dynamical systems[J].IEEE Transactions on Neural Networks,2010,21(10):1599-1609.
[22] Chai R,Ling S H,San P P,Naik G R,et al.Improving EEG-based driver fatigue classification using sparse-deep belief networks[J].Frontiers in Neuroscience,2017,11:103-117.

基于Gamma深度信念网络的飞行员脑疲劳状态识别

Pilots' Brain Fatigue State Inference Based on Gamma Deep Belief Network

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

[1]	李明爱, 张圆圆. 基于连续小波变换和符号传递熵的脑功能网络构建方法[J]. 电子学报, 2022, 50(7): 1600-1608.
[2]	司慧芳, 谢天, 高军峰, 官金安, 向州州, 兰长有, 卿训华. 基于相位延迟指数的脑功能网络及测谎研究[J]. 电子学报, 2018, 46(7): 1742-1747.
[3]	高军峰, 司慧芳, 余彬, 顾凌云, 梁莹, 杨勇. 基于脑电样本熵的测谎分析[J]. 电子学报, 2017, 45(8): 1836-1841.
[4]	刘志勇, 孙金玮. 基于序列连通度的睡眠分期算法研究[J]. 电子学报, 2017, 45(1): 225-231.
[5]	张少白, 王勇, 刘友谊. 基于DIVA模型的脑电信号去噪方法研究[J]. 电子学报, 2015, 43(4): 700-707.
[6]	余南南, 刘海宽, 王晓燕. 联合稀疏表示的双次诱发电位提取算法[J]. 电子学报, 2014, 42(5): 852-857.
[7]	王登, 苗夺谦, 王睿智. 一种新的基于小波包分解的EEG特征抽取与识别方法研究[J]. 电子学报, 2013, 41(1): 193-198.
[8]	罗志增;曹铭. 基于最大信噪比盲源分离的脑电信号伪迹滤波算法[J]. 电子学报, 2011, 39(12): 2926-2931.