Literature DB >> 24860041

Wireless and wearable EEG system for evaluating driver vigilance.

Chin-Teng Lin, Chun-Hsiang Chuang, Chih-Sheng Huang, Shu-Fang Tsai, Shao-Wei Lu, Yen-Hsuan Chen, Li-Wei Ko.   

Abstract

Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.

Entities:  

Mesh:

Year:  2014        PMID: 24860041     DOI: 10.1109/TBCAS.2014.2316224

Source DB:  PubMed          Journal:  IEEE Trans Biomed Circuits Syst        ISSN: 1932-4545            Impact factor:   3.833


  22 in total

1.  Passive BCI based on drowsiness detection: an fNIRS study.

Authors:  M Jawad Khan; Keum-Shik Hong
Journal:  Biomed Opt Express       Date:  2015-09-22       Impact factor: 3.732

2.  Sleepiness and Driving: Multidimensional Legal, Social, Technological, and Biological Challenges.

Authors:  Robert Joseph Thomas
Journal:  Sleep       Date:  2016-05-01       Impact factor: 5.849

3.  Comparison of Eye and Face Features on Drowsiness Analysis.

Authors:  I-Hsi Kao; Ching-Yao Chan
Journal:  Sensors (Basel)       Date:  2022-08-30       Impact factor: 3.847

4.  Three-Dimensional Brain-Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks.

Authors:  Jianjun Meng; Taylor Streitz; Nicholas Gulachek; Daniel Suma; Bin He
Journal:  IEEE Trans Biomed Eng       Date:  2018-10-01       Impact factor: 4.538

5.  A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

Authors:  Zutao Zhang; Dianyuan Luo; Yagubov Rasim; Yanjun Li; Guanjun Meng; Jian Xu; Chunbai Wang
Journal:  Sensors (Basel)       Date:  2016-02-19       Impact factor: 3.576

6.  Physiological artifacts in scalp EEG and ear-EEG.

Authors:  Simon L Kappel; David Looney; Danilo P Mandic; Preben Kidmose
Journal:  Biomed Eng Online       Date:  2017-08-11       Impact factor: 2.819

7.  On the Keyhole Hypothesis: High Mutual Information between Ear and Scalp EEG.

Authors:  Kaare B Mikkelsen; Preben Kidmose; Lars K Hansen
Journal:  Front Hum Neurosci       Date:  2017-06-30       Impact factor: 3.169

8.  Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.

Authors:  Rifai Chai; Sai Ho Ling; Phyo Phyo San; Ganesh R Naik; Tuan N Nguyen; Yvonne Tran; Ashley Craig; Hung T Nguyen
Journal:  Front Neurosci       Date:  2017-03-07       Impact factor: 4.677

9.  A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness.

Authors:  Gang Li; Wan-Young Chung
Journal:  Sensors (Basel)       Date:  2015-08-21       Impact factor: 3.576

10.  Predicting task performance from biomarkers of mental fatigue in global brain activity.

Authors:  Lin Yao; Jonathan L Baker; Nicholas D Schiff; Keith P Purpura; Mahsa Shoaran
Journal:  J Neural Eng       Date:  2021-03-08       Impact factor: 5.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.