Feature Extraction of Seafarers' Workload Based on EEG Signals

被引：0
|
作者：
Chen, Jinglei [1 ,2 ]
Fan, Shiqi [1 ,2 ,3 ]
Zhang, Jinfen [1 ]
Tian, Wuliu [4 ,5 ]
机构：
[1] Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan, Peoples R China
[2] Natl Engn Res Ctr Water Transport Safety WTSC, Wuhan, Peoples R China
[3] Liverpool John Moores Univ, Liverpool Logist Offshore & Marine, Liverpool, Merseyside, England
[4] Beibu Gulf Univ, Maritime Coll, Qinzhou, Peoples R China
[5] Beibu Gulf Univ, Qinzhou Maritime Nav & Antifouling Key Lab, Qinzhou, Peoples R China
来源：
2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-QINGDAO) | 2019年
基金：
欧盟地平线“2020”; 中国国家自然科学基金; 美国国家科学基金会;
关键词：
Maritime safety; Human errors; Bridge simulator; Mental workload;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Most of maritime accidents have been proved to be caused by human errors. In order to quantitatively analyze human factor of seafarers, we carried out the simulation test of ship operation. During the test, a brain instrument was used to collect Electroencephalogram (EEG) signals of the subjects.In the current research stage, EEG signals have been denoised by wavelet transform, and EEG features are extracted by finite impulse response(FIR) digital filter. The feature can be used as the feature vector of Support Vector Machine(SVM) classifier for training, and finally a classifier for workload recognition is established.
引用
收藏
页数：6
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