文献类型：期刊文献

英文题名：Drowsiness Detection by Bayesian-Copula Discriminant Classifier Based on EEG Signals During Daytime Short Nap

作者：Qian, Dong[1];Wang, Bei[1];Qing, Xiangyun[1];Zhang, Tao[2];Zhang, Yu[1];Wang, Xingyu[1];Nakamura, Masatoshi[3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Tsinghua Univ, Dept Automat, Beijing, Peoples R China;[3]Saga Univ, Res Inst Syst Control, Inst Adv Res & Educ, Saga, Japan

年份：2017

卷号：64

期号：4

起止页码：743

外文期刊名：IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

收录：;EI(收录号：20171603587971);WOS:【SCI-EXPANDED(收录号:WOS:000398738300002)】；

基金：This work was supported in part by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Shanghai Natural Science Foundation under Grant 16ZR1407500, and the Medical Cooperation Project by Shanghai Municipal Science and Technology Commission under Grant 12DZ1940903. Asterisk indicates corresponding author.

语种：英文

外文关键词：Bayesian-copula discriminant classifier (BCDC); daytime short nap; drowsiness detection; electroencephalogram (EEG); kernel density estimation (KDE)

摘要：Objective: Daytime short nap involves individual physiological states including alertness and drowsiness. In order to have a better understanding of the periodical rhymes of physiological states and then promote a good interpretability of alertness, the aim of this study is to detect drowsiness during daytime short nap. Methods: A method of Bayesian-copula discriminant classifier (BCDC) was introduced to detect individual drowsiness based on the physiological features extracted from electroencephalogram (EEG) signals. As an extension of traditional Bayesian decision theory, the BCDC method tries to construct the class-conditional probability density functions by exploiting the theory of copula and kernel density estimation. Results: The proposed BCDC method was validated with experimental dataset and compared with other traditional methods for drowsiness detection. The obtained results showed that our method outperformed other methods in terms of three evaluation criteria. Conclusion: Our proposed method is effective to detect drowsiness with superior performance. Additionally, the BCDC method is relatively robust to different parameter settings on the group-level dataset. Significance: The proposed method is likely to be a useful tool to improve the correctness of the estimated class-conditional probability density functions. Since features are extracted from spontaneous EEG recordings, the results of this study can be further generalized to other experimental environment to detect vigilance level or driver drowsiness.