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
相关论文
共 50 条
  • [41] Classification of Sleep Disorders Based on EEG Signals by using Feature Extraction Techniques with KNN Classifier
    Dhongade, Dayanand Vishwanath
    Rao, T. V. K. H.
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON INNOVATIONS IN GREEN ENERGY AND HEALTHCARE TECHNOLOGIES (IGEHT), 2017,
  • [42] Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals
    Ein Shoka A.A.
    Alkinani M.H.
    El-Sherbeny A.S.
    El-Sayed A.
    Dessouky M.M.
    [J]. Brain Informatics, 2021, 8 (01)
  • [43] A Novel Feature Extraction Method for Epilepsy EEG Signals Based on Robust Generalized Synchrony Analysis
    Li Shunan
    Li Donghui
    Deng Bin
    Wei Xile
    Wang Jiang
    Wai-Loc Chan
    [J]. 2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2013, : 5144 - 5147
  • [44] Research on feature extraction method based on brain network and CSP for MI-EEG signals
    Yu, Rui
    Yin, Kuiying
    [J]. PROCEEDINGS OF THE 2020 17TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD 2020), 2020, : 668 - 674
  • [45] Feature Extraction Method for EEG based Biometrics
    Li, Sukun
    Cha, Sung-Hyuk
    [J]. 2019 3RD INTERNATIONAL CONFERENCE ON MACHINE VISION AND INFORMATION TECHNOLOGY (CMVIT 2019), 2019, 1229
  • [46] Feature extraction method for classification of alertness and drowsiness states EEG signals
    Bajaj, Varun
    Taran, Sachin
    Khare, Smith K.
    Sengur, Abdulkadir
    [J]. APPLIED ACOUSTICS, 2020, 163
  • [47] Research on Algorithm for Feature Extraction and Classification of Motor Imagery EEG Signals
    Tian, Juan
    Zhang, Zhaochen
    [J]. 2016 INTERNATIONAL CONFERENCE ON MEDICINE SCIENCES AND BIOENGINEERING (ICMSB2016), 2017, 8
  • [48] Feature Extraction and Classification of EEG Signals for Mapping Motor Area of the Brain
    Sita, J.
    Nair, G. J.
    [J]. 2013 INTERNATIONAL CONFERENCE ON CONTROL COMMUNICATION AND COMPUTING (ICCC), 2013, : 463 - +
  • [49] A Theory of Kernel Extreme Energy Difference for Feature Extraction of EEG Signals
    Sun, Shiliang
    Li, Jinbo
    [J]. ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS, 2009, 5678 : 636 - 643
  • [50] Fourier transform for feature extraction of Electro Encephalo Graph (EEG) signals
    Hindarto, H.
    Muntasa, A.
    Sumarno, S.
    [J]. 4TH ANNUAL APPLIED SCIENCE AND ENGINEERING CONFERENCE, 2019, 2019, 1402
12345