• Users Online: 323
  • Print this page
  • Email this page

    Article Cited by others

ORIGINAL ARTICLE

EEG-based drowsiness detection for safe driving using chaotic features and statistical tests

Mardi Zahra, Ashtiani Seyedeh Naghmeh Miri, Mikaili Mohammad

Year : 2011| Volume: 1| Issue : 2 | Page no: 130-137

   This article has been cited by
 
1 Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees
Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Hossein Ahmadi, Sarminah Samad, Abdullah Alghamdi, Mesfer Alrizq, Sultan Alyami, Fatima Khan Nayer
Heliyon. 2023; : e15258
[Pubmed]  [Google Scholar] [DOI]
2 Designing an XAI Interface for BCI Experts: A Contextual Design for Pragmatic Explanation Interface Based on Domain Knowledge in a Specific Context
Sangyeon Kim, Sanghyun Choo, Donghyun Park, Hoonseok Park, Chang S. Nam, Jae-Yoon Jung, Sangwon Lee
International Journal of Human-Computer Studies. 2023; : 103009
[Pubmed]  [Google Scholar] [DOI]
3 Nonlinear and machine learning analyses on high-density EEG data of math experts and novices
Hanna Poikonen, Tomasz Zaluska, Xiaying Wang, Michele Magno, Manu Kapur
Scientific Reports. 2023; 13(1)
[Pubmed]  [Google Scholar] [DOI]
4 4D: A Real-Time Driver Drowsiness Detector Using Deep Learning
Israt Jahan, K. M. Aslam Uddin, Saydul Akbar Murad, M. Saef Ullah Miah, Tanvir Zaman Khan, Mehedi Masud, Sultan Aljahdali, Anupam Kumar Bairagi
Electronics. 2023; 12(1): 235
[Pubmed]  [Google Scholar] [DOI]
5 Drowsy Detection System For bus and Car Drivers
Amit Shukla, Nirmal Singh, Rahul Kumar, Anand .
SSRN Electronic Journal. 2022;
[Pubmed]  [Google Scholar] [DOI]
6 Driver Fatigue and Distracted Driving Detection Using Random Forest and Convolutional Neural Network
Bing-Ting Dong, Huei-Yung Lin, Chin-Chen Chang
Applied Sciences. 2022; 12(17): 8674
[Pubmed]  [Google Scholar] [DOI]
7 Drowsiness Detection Using Ocular Indices from EEG Signal
Sreeza Tarafder, Nasreen Badruddin, Norashikin Yahya, Arbi Haza Nasution
Sensors. 2022; 22(13): 4764
[Pubmed]  [Google Scholar] [DOI]
8 Investigating the role of data preprocessing, hyperparameters tuning, and type of machine learning algorithm in the improvement of drowsy EEG signal modeling
Farbod Farhangi
Intelligent Systems with Applications. 2022; : 200100
[Pubmed]  [Google Scholar] [DOI]
9 Driver drowsiness estimation using EEG signals with a dynamical encoder–decoder modeling framework
Sadegh Arefnezhad, James Hamet, Arno Eichberger, Matthias Frühwirth, Anja Ischebeck, Ioana Victoria Koglbauer, Maximilian Moser, Ali Yousefi
Scientific Reports. 2022; 12(1)
[Pubmed]  [Google Scholar] [DOI]
10 Epilepsy-Net: attention-based 1D-inception network model for epilepsy detection using one-channel and multi-channel EEG signals
Abdelhamid Lebal, Abdelouahab Moussaoui, Abdelmounaam Rezgui
Multimedia Tools and Applications. 2022;
[Pubmed]  [Google Scholar] [DOI]
11 A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms
Islam A. Fouad
Ain Shams Engineering Journal. 2022; : 101895
[Pubmed]  [Google Scholar] [DOI]
12 Hybrid classification model for eye state detection using electroencephalogram signals
Shwet Ketu,Pramod Kumar Mishra
Cognitive Neurodynamics. 2021;
[Pubmed]  [Google Scholar] [DOI]
13 Sensitivity and specificity of the driver sleepiness detection methods using physiological signals: A systematic review
Christopher N. Watling,Md Mahmudul Hasan,Grégoire S. Larue
Accident Analysis & Prevention. 2021; 150: 105900
[Pubmed]  [Google Scholar] [DOI]
14 Entropy-Based Drowsiness Detection Using Adaptive Variational Mode Decomposition
Smith K. Khare, Varun Bajaj
IEEE Sensors Journal. 2021; 21(5): 6421
[Pubmed]  [Google Scholar] [DOI]
15 Is it Good or Bad to Provide Driver Fatigue Warning During Take-Over in Highly Automated Driving?
Jianwei Niu, Chuang Ma
Transportation Research Record: Journal of the Transportation Research Board. 2021; : 0361198121
[Pubmed]  [Google Scholar] [DOI]
16 Vision-Based Road Rage Detection Framework in Automotive Safety Applications
Alessandro Leone,Andrea Caroppo,Andrea Manni,Pietro Siciliano
Sensors. 2021; 21(9): 2942
[Pubmed]  [Google Scholar] [DOI]
17 Non-Invasive Driver Drowsiness Detection System
Hafeez Ur Rehman Siddiqui,Adil Ali Saleem,Robert Brown,Bahattin Bademci,Ernesto Lee,Furqan Rustam,Sandra Dudley
Sensors. 2021; 21(14): 4833
[Pubmed]  [Google Scholar] [DOI]
18 Real-Time Driver Drowsiness Detection using Computer Vision
Mahek Jain,Bhavya Bhagerathi,Dr. Sowmyarani C N
International Journal of Engineering and Advanced Technology. 2021; 11(1): 109
[Pubmed]  [Google Scholar] [DOI]
19 ICONet: A Lightweight Network with Greater Environmental Adaptivity
Wei He,Yanmei Huang,Zanhao Fu,Yingcheng Lin
Symmetry. 2020; 12(12): 2119
[Pubmed]  [Google Scholar] [DOI]
20 Overview of approaches to driver fatigue recognition and existing technical solutions
Ya. D Saprykin, V. I Ryazantsev, A. A Smirnov
Izvestiya MGTU MAMI. 2020; 14(3): 48
[Pubmed]  [Google Scholar] [DOI]
21 A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection
John LaRocco,Minh Dong Le,Dong-Guk Paeng
Frontiers in Neuroinformatics. 2020; 14
[Pubmed]  [Google Scholar] [DOI]
22 Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with Novel Temporal Measures on Eye States
Kenta NISHIYUKI,Jia-Yau SHIAU,Shigenori NAGAE,Tomohiro YABUUCHI,Koichi KINOSHITA,Yuki HASEGAWA,Takayoshi YAMASHITA,Hironobu FUJIYOSHI
IEICE Transactions on Information and Systems. 2020; E103.D(6): 1276
[Pubmed]  [Google Scholar] [DOI]
23 V2iFi
Tianyue Zheng,Zhe Chen,Chao Cai,Jun Luo,Xu Zhang
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2020; 4(2): 1
[Pubmed]  [Google Scholar] [DOI]
24 Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation
Qianyang Zhuang,Zhang Kehua,Jiayi Wang,Qianqian Chen
IEEE Access. 2020; 8: 173440
[Pubmed]  [Google Scholar] [DOI]
25 Feature extraction method for classification of alertness and drowsiness states EEG signals
Varun Bajaj,Sachin Taran,Smith K. Khare,Abdulkadir Sengur
Applied Acoustics. 2020; 163: 107224
[Pubmed]  [Google Scholar] [DOI]
26 Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity
Amirmasoud Ahmadi,Hanieh Bazregarzadeh,Kamran Kazemi
Biocybernetics and Biomedical Engineering. 2020;
[Pubmed]  [Google Scholar] [DOI]
27 Frequency–amplitude coupling: a new approach for decoding of attended features in covert visual attention task
Saeideh Davoudi,Amirmasoud Ahmadi,Mohammad Reza Daliri
Neural Computing and Applications. 2020;
[Pubmed]  [Google Scholar] [DOI]
28 Optimized Tunable Q Wavelet Transform Based Drowsiness Detection from Electroencephalogram Signals
S.K. Khare,V. Bajaj
IRBM. 2020;
[Pubmed]  [Google Scholar] [DOI]
29 Eye gaze pattern analysis for fatigue detection based on GP-BCNN with ESM
Yan Wang,Rui Huang,Lei Guo
Pattern Recognition Letters. 2019; 123: 61
[Pubmed]  [Google Scholar] [DOI]
30 Can we feel like being neither alert nor sleepy? The electroencephalographic signature of this subjective sub-state of wake state yields an accurate measure of objective sleepiness level
Arcady A. Putilov,Olga G. Donskaya,Evgeniy G. Verevkin
International Journal of Psychophysiology. 2019; 135: 33
[Pubmed]  [Google Scholar] [DOI]
31 Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods
Çigdem Inan Aci,Murat Kaya,Yuriy Mishchenko
Expert Systems with Applications. 2019; 134: 153
[Pubmed]  [Google Scholar] [DOI]
32 Driver Drowsiness Measurement Technologies: Current Research, Market Solutions, and Challenges
M. Doudou,A. Bouabdallah,V. Berge-Cherfaoui
International Journal of Intelligent Transportation Systems Research. 2019;
[Pubmed]  [Google Scholar] [DOI]
33 Learning-Based Instantaneous Drowsiness Detection Using Wired and Wireless Electroencephalography
Hyun-Soo Choi,Seonwoo Min,Siwon Kim,Ho Bae,Jee-Eun Yoon,Inha Hwang,Dana Oh,Chang-Ho Yun,Sungroh Yoon
IEEE Access. 2019; 7: 146390
[Pubmed]  [Google Scholar] [DOI]
34 Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units
Zhitao Xiao,Zhiqiang Hu,Lei Geng,Fang Zhang,Jun Wu,Yuelong Li
IET Intelligent Transport Systems. 2019; 13(9): 1410
[Pubmed]  [Google Scholar] [DOI]
35 Semi-cascade network for driver’s distraction recognition
Jun Hu,Wei Liu,Jiawen Kang,Wenxing Yang,Hong Zhao
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2019; 233(9): 2323
[Pubmed]  [Google Scholar] [DOI]
36 Automatic Detection of Driver Impairment Based on Pupillary Light Reflex
Alessandro Amodio,Michele Ermidoro,Davide Maggi,Simone Formentin,Sergio Matteo Savaresi
IEEE Transactions on Intelligent Transportation Systems. 2019; 20(8): 3038
[Pubmed]  [Google Scholar] [DOI]
37 An Investigation of the Effects of Changes in the Indoor Ambient Temperature on Arousal Level, Thermal Comfort, and Physiological Indices
Jongseong Gwak,Motoki Shino,Kazutaka Ueda,Minoru Kamata
Applied Sciences. 2019; 9(5): 899
[Pubmed]  [Google Scholar] [DOI]
38 Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram
Mikito Ogino,Yasue Mitsukura
Sensors. 2018; 18(12): 4477
[Pubmed]  [Google Scholar] [DOI]
39 Automated EEG Artifact Handling With Application in Driver Monitoring
Shaibal Barua, Mobyen Uddin Ahmed, Christer Ahlstrom, Shahina Begum, Peter Funk
IEEE Journal of Biomedical and Health Informatics. 2018; 22(5): 1350
[Pubmed]  [Google Scholar] [DOI]
40 Drowsiness Detection Using Adaptive Hermite Decomposition and Extreme Learning Machine for Electroencephalogram Signals
Sachin Taran, Varun Bajaj
IEEE Sensors Journal. 2018; 18(21): 8855
[Pubmed]  [Google Scholar] [DOI]
41 Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques
Rateb Jabbar,Khalifa Al-Khalifa,Mohamed Kharbeche,Wael Alhajyaseen,Mohsen Jafari,Shan Jiang
Procedia Computer Science. 2018; 130: 400
[Pubmed]  [Google Scholar] [DOI]
42 Random eye state change detection in real-time using EEG signals
Abolfazl Saghafi,Chris P. Tsokos,Mahdi Goudarzi,Hamidreza Farhidzadeh
Expert Systems with Applications. 2017; 72: 42
[Pubmed]  [Google Scholar] [DOI]
43 Effect of angle of deposition on the Fractal properties of ZnO thin film surface
R.P. Yadav,D.C. Agarwal,Manvendra Kumar,Parasmani Rajput,D.S. Tomar,S.N. Pandey,P.K. Priya,A.K. Mittal
Applied Surface Science. 2017; 416: 51
[Pubmed]  [Google Scholar] [DOI]
44 Challenges in detecting drowsiness based on driver’s behavior
V Triyanti,H Iridiastadi
IOP Conference Series: Materials Science and Engineering. 2017; 277: 012042
[Pubmed]  [Google Scholar] [DOI]
45 Common spatial pattern method for real-time eye state identification by using electroencephalogram signals
Abolfazl Saghafi,Chris P. Tsokos,Hamidreza Farhidzadeh
IET Signal Processing. 2017; 11(8): 936
[Pubmed]  [Google Scholar] [DOI]
46 Generalizability of Frequency Weighting Curve for Extraction of Spectral Drowsy Component From the EEG Signals Recorded in Eyes-Closed Condition
Arcady A. Putilov,Olga G. Donskaya,Evgeniy G. Verevkin
Clinical EEG and Neuroscience. 2017; 48(4): 259
[Pubmed]  [Google Scholar] [DOI]
47 Wearable Glove-Type Driver Stress Detection Using a Motion Sensor
Boon-Giin Lee, Wan-Young Chung
IEEE Transactions on Intelligent Transportation Systems. 2017; 18(7): 1835
[Pubmed]  [Google Scholar] [DOI]
48 A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability
Muhammad Awais,Nasreen Badruddin,Micheal Drieberg
Sensors. 2017; 17(9): 1991
[Pubmed]  [Google Scholar] [DOI]
49 Mental Fatigue Monitoring Using a Wearable Transparent Eye Detection System
Kota Sampei,Miho Ogawa,Carlos Torres,Munehiko Sato,Norihisa Miki
Micromachines. 2016; 7(2): 20
[Pubmed]  [Google Scholar] [DOI]
50 APPLICATION OF S-TRANSFORM FOR AUTOMATED DETECTION OF VIGILANCE LEVEL USING EEG SIGNALS
R. UPADHYAY,P. K. PADHY,P. K. KANKAR
Journal of Biological Systems. 2016; 24(01): 1
[Pubmed]  [Google Scholar] [DOI]
51 A comparative study of feature ranking techniques for epileptic seizure detection using wavelet transform
R. Upadhyay,P.K. Padhy,P.K. Kankar
Computers & Electrical Engineering. 2016; 53: 163
[Pubmed]  [Google Scholar] [DOI]
52 Channel optimization and nonlinear feature extraction for Electroencephalogram signals classification
R. Upadhyay,A. Manglick,D.K. Reddy,P.K. Padhy,P.K. Kankar
Computers & Electrical Engineering. 2015; 45: 222
[Pubmed]  [Google Scholar] [DOI]
53 Is a two-dimensional generalization of the Higuchi algorithm really necessary?
Helmut Ahammer,Nikolaus Sabathiel,Martin A. Reiss
Chaos: An Interdisciplinary Journal of Nonlinear Science. 2015; 25(7): 073104
[Pubmed]  [Google Scholar] [DOI]
54 In-Flight Automatic Detection of Vigilance States Using a Single EEG Channel
F. Sauvet,C. Bougard,M. Coroenne,L. Lely,P. Van Beers,M. Elbaz,M. Guillard,D. Leger,M. Chennaoui
IEEE Transactions on Biomedical Engineering. 2014; 61(12): 2840
[Pubmed]  [Google Scholar] [DOI]

 

Read this article