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中国生物医学工程学报  2022, Vol. 41 Issue (4): 402-411    DOI: 10.3969/j.issn.0258-8021.2022.04.003
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一种疲劳驾驶检测中的脑电信号通道选择方法
郑赟1, 马玉良1,2*, 孙明旭3, 申涛3, 张建海2,4, 佘青山1,2
1(杭州电子科技大学自动化学院,杭州 310018)
2(浙江省脑机协同智能重点实验室,杭州 310018)
3(济南大学自动化与电气工程学院,济南 250022)
4(杭州电子科技大学计算机学院,杭州 310018)
A Channel Selection Method Using EEG Signals for Fatigue Driving Detection
Zheng Yun1, Ma Yuliang1,2*, Sun Mingxu3, Shen Tao3, Zhang Jianhai2,4, She Qingshan1,2
1(School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China)
2(Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China)
3(School of Electrical Engineering, University of Jinan, Jinan 250022, China)
4(College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)
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摘要 针对传统的基于全通道脑电信号(EEG)的疲劳驾驶检测方法中存在数据冗余和硬件设施复杂的问题,提出一种基于阈值筛选的通道选择方法。首先对多个特征计算标准差等指标分别筛选出每个特征各自的理想通道;其次使用多层感知超限学习机(H-ELM)和使用粒子群优化算法(PSO)优化后的多层感知超限学习机(PSO-H-ELM)分别对理想通道的数据进行二分类, 并且与全通道数据的分类结果进行对比。分别采用了2组实验数据(一组数据通过实验室的模拟驾驶设备采集,受试6人;一组数据来自于公开数据集,受试12人;两者采集设备不同)对提出的方法进行了验证。实验结果表明,对于18名受试者,使用集合经验模态分解(EEMD)所获得的有限个本征模函数(IMF)的功率谱(PSD)特征普遍能够得到较多理想通道,并且对于同一设备,理想通道的分布大致相同并且通道数较少(分别为8个和11个通道)。同时,此通道选择方法还极大提高了疲劳驾驶检测的分类准确率(18 名被试在使用理想通道数据下的平均准确率达到了99.75 %,比使用全通道数据的准确率提高19.36 %)。此外,样本熵的理想通道与功率谱的理想通道几乎不重合,说明两种特征具有很好的互补性,两者特征结合提高了本方法的实用性,在疲劳驾驶检测的应用上具有一定参考价值。
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郑赟
马玉良
孙明旭
申涛
张建海
佘青山
关键词 疲劳驾驶检测脑电信号集合经验模态分解多层感知超限学习机    
Abstract:Aiming at the problems of data redundancy and complex hardware facilities in the traditional fatigue detection method based on EEG, in this work, a channel selection method based on threshold filtering was proposed and used to select the optimal channel for several features by calculating the standard deviation and other metrics. Furthermore, hierarchical extreme learning machine (H-ELM) and PSO-H-ELM optimized by particle swarm optimization (PSO) were used to classify the selected optimal channel data and compared the accuracy to the results obtained by all the channels. The proposed method was validated using two groups of experimental data (one dataset was collected by a laboratory driving simulation device with 6 subjects and one dataset was a public dataset with 12 subjects and different collection devices). The results showed that for 18 subjects, many optimal channels were obtained from the ensemble empirical mode decomposition (EEMD) by using the power spectral density (PSD) feature of intrinsic mode function (IMF), and the distribution of optimal channels was roughly the same and the number of channels was small for the same device (8 and 11 channels, respectively). In addition, this channel selection method also significantly improved the accuracy of fatigue driving detection (the average accuracy of 18 subjects using the optimal channels was 99.75 %, 19.36 % higher than that using the full channel). Moreover, the ideal channel of sample entropy (SE) and the ideal channel of PSD hardly overlap, indicating that the two features were well complementary, and the practicality of proposed method is greatly improved by combining these 2 features. The results proved the effectiveness of the proposed method, which was of value in the application of fatigue driving detection.
Key wordsfatigue driving detection    EEG    ensemble empirical mode decomposition    hierarchical extreme learning machine
收稿日期: 2021-07-29     
基金资助:国家自然科学基金(62071161, 61871427);山东省重点研发计划项目(2019JZZY021005)
通讯作者: *E-mail: mayuliang@hdu.edu.cn   
引用本文:   
郑赟, 马玉良, 孙明旭, 申涛, 张建海, 佘青山. 一种疲劳驾驶检测中的脑电信号通道选择方法[J]. 中国生物医学工程学报, 2022, 41(4): 402-411.
Zheng Yun, Ma Yuliang, Sun Mingxu, Shen Tao, Zhang Jianhai, She Qingshan. A Channel Selection Method Using EEG Signals for Fatigue Driving Detection. Chinese Journal of Biomedical Engineering, 2022, 41(4): 402-411.
链接本文:  
http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2022.04.003     或     http://cjbme.csbme.org/CN/Y2022/V41/I4/402
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