一种基于核主成分分析的驾驶疲劳状态预测方法
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摘要
针对在驾驶疲劳状态识别研究中采集的脑电信号(Electroencephalograph,EEG)具有非线性特点,以及目前基于EEG的驾驶疲劳状态识别方法仍存在识别准确率不很理想的问题,提出基于样本熵和核主成分分析的驾驶疲劳状态识别方法。所研究的方法结合了基于样本熵在驾驶疲劳状态识别方法具有较好的识别准确率优势以及核主成分对主成分是非线性的情况下具有较好的降维效果优势。实验结果表明所提出的方法是有效的。
The EEG data acquired in the study of driving fatigue state recognition has nonlinear characteristics,and the current recognition method based on EEG for driver fatigue state still has the problem that the recognition accuracy rate is not ideal.This paper proposes a driving fatigue identification method based on the sample entropy(SE) and the kernel principal component analysis(KPCA).The proposed method combines the advantages better recognition accuracy of SE in the recognition of driving fatigue state and the benefits of satisfactory dimensionality reduction of KPCA.The experimental results show that the proposed method is effective.
The EEG data acquired in the study of driving fatigue state recognition has nonlinear characteristics,and the current recognition method based on EEG for driver fatigue state still has the problem that the recognition accuracy rate is not ideal.This paper proposes a driving fatigue identification method based on the sample entropy(SE) and the kernel principal component analysis(KPCA).The proposed method combines the advantages better recognition accuracy of SE in the recognition of driving fatigue state and the benefits of satisfactory dimensionality reduction of KPCA.The experimental results show that the proposed method is effective.
引文
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[11] 陆荣秀,杨辉,欧阳超明,等.基于PCA_LS_SVM的镨_钕萃取过程元素组分含量预测[J].南昌大学学报(理科版),2013,37(6):589-593.
[12] 关俊强,杨帮华,马世伟,袁玲.基于PCA及SVM的运动想象脑电信号识别研究[J].北京生物医学工程,2010,29(3):261-265.
[13] 袁瑞,魏庆国.基于小波包分解与近似熵的脑电特征提取方法研究及在脑机接口中的应用[J].南昌大学学报(理科版),2017,41(3):282-287.
[2] 李晓峰,马锦飞.驾驶疲劳的理论分类及影响因素[J].决策探索(下半月),2017(8):87.
[3] 郭孜政,牛琳博.基于心电信号的驾驶疲劳巧别方法研究[D].成都:西南交通大学,2017.
[4] 杨立才,李发权.基于眼动与脉搏信息融合的驾驶疲劳识别算法研究[D].济南:山东大学,2015.
[5] ZHAO C L,ZHAO M,YANG Y.The reorganization of human brain networks modulated by driving mental fatigue[J].IEEE Journal of Biomedical and Health Informatics,2017,21(3):743-755.
[6] ZHAO X P,MENG C N,FENG M K.Fatigue detection based oncascade convolutional neural network[J].Journal of Optoelectronics Laser,2017,28(5):497-502.
[7] 张宁宁,王宏,付荣荣.基于小波熵的驾驶疲劳脑电信号特征提取[J].汽车工程,2013,35(12):1139-1142.
[8] 彭军强,吴平东,殷罡.疲劳驾驶的脑电特性探索[J].北京理工大学报,2007,27(7):585-590.
[9] 丁晓慧,马玉良.运动想象脑电信号特征提取与分类研究[D].杭州:杭州电子科技大学,2016.
[10] 赵庆敏,彭雪莹.Curvelet变换结合(2D)～2PCA的人脸识别算[J].南昌大学学报(理科版),2018,42(2):180-183.
[11] 陆荣秀,杨辉,欧阳超明,等.基于PCA_LS_SVM的镨_钕萃取过程元素组分含量预测[J].南昌大学学报(理科版),2013,37(6):589-593.
[12] 关俊强,杨帮华,马世伟,袁玲.基于PCA及SVM的运动想象脑电信号识别研究[J].北京生物医学工程,2010,29(3):261-265.
[13] 袁瑞,魏庆国.基于小波包分解与近似熵的脑电特征提取方法研究及在脑机接口中的应用[J].南昌大学学报(理科版),2017,41(3):282-287.