吴仕超1,
刘少林2,
张亚徽2,
魏颖2
1.东北大学机器人科学与工程学院? ?沈阳? ?110169
2.东北大学信息科学与工程学院? ?沈阳? ?110819
基金项目:中央高校基本科研业务费专项基金(N172608005)，辽宁省科学事业公益研究基金(20170021)
详细信息
作者简介:王斐：男，1974年生，博士，副教授，研究方向为人机交互感知与协作理论与技术、仿人机器人理论与技术
吴仕超：男，1996年生，硕士生，研究方向为机器学习、脑电认知
刘少林：男，1993年生，硕士生，研究方向为模式识别、深度学习
张亚徽：女，1995年生，硕士生，研究方向为机器学习、脑机接口
魏颖：女，1968年生，博士，教授，主要研究方向为图像处理与模式识别、医学影像计算与分析
通讯作者:王斐 wangfei@mail.neu.edu.cn
中图分类号:TP391计量
文章访问数:2005
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被引次数:0
出版历程
收稿日期:2018-09-20
修回日期:2019-02-17
网络出版日期:2019-03-21
刊出日期:2019-09-10
Driver Fatigue Detection Through Deep Transfer Learning in an Electroencephalogram-based System
Fei WANG1,,,Shichao WU1,
Shaolin LIU2,
Yahui ZHANG2,
Ying WEI2
1. Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169 China
2. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Funds:The Fundamental Research Funds for the Central Universities (N172608005), The Scientific Research Foundation for Public Welfare of Liaoning Province (20170021)
摘要
摘要:脑电信号一直被誉为疲劳检测的“金标准”，驾驶者的精神状态可通过对脑电信号的分析得到。但由于脑电信号具有非线性、非平稳性和空间分辨率低等特点，传统的机器学习方法在运用脑电信号进行疲劳检测时还存在识别率低，特征提取操作繁琐等不足。为此，该文基于脑电信号的电极-频率分布图，提出运用深度迁移学习实现的驾驶疲劳检测方法，即搭建深度卷积神经网络，并利用SEED脑电情绪数据集对其进行预训练，然后通过迁移学习方法将其用于驾驶疲劳检测。实验结果表明，卷积神经网络模型能够很好地从电极-频率分布图中获得与疲劳状态相关的特征信息，达到较好的识别效果。此外，基于迁移学习策略可以将训练好的深度网络模型迁移到其他识别任务上，有助于推动脑电信号在驾驶疲劳检测系统中的应用。
关键词:脑电信号/
疲劳检测/
迁移学习/
卷积神经网络/
电极-频率分布图
Abstract:ElectroEncephaloGram (EEG) is regarded as a " gold standard” of fatigue detection and drivers’ vigilance states can be detected through the analysis of EEG signals. However, due to the characteristics of non-linear, non-stationary and low spatial resolution of EEG signals, traditional machine learning methods still have the disadvantages of low recognition rate and complicated feature extraction operations in EEG-based fatigue detection task. To tackle this problem, a fatigue detection method with transfer learning based on the Electrode-Frequency Distribution Maps (EFDMs) of EEG signals is proposed. A deep convolutional neural network is designed and pre-trained with SEED dataset, and then it is used for fatigue detection with transfer learning strategy. Experimental results show that the proposed convolutional neural network can automatically obtain vigilance related features from EFDMs, and achieve much better recognition results than traditional machine learning methods. Moreover, based on the transfer learning strategy, this model can also be used for other recognition tasks, which is helpful for promoting the application of EEG signals to the driver fatigue detection system.
Key words:ElectroEncephaloGram (EEG)/
Fatigue detection/
Transfer learning/
Convolutional neural network/
Electrode-frequency distribution maps
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