CORE
CO
nnecting
RE
positories
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Research partnership
About
About
About us
Our mission
Team
Blog
FAQs
Contact us
Community governance
Governance
Advisory Board
Board of supporters
Research network
Innovations
Our research
Labs
research
Single channel wireless EEG device for real-time fatigue level detection
Authors
CH Chuang
TY Hsiung
+7 more
LW Ko
WK Lai
WG Liang
CT Lin
SW Lu
YC Lu
HH Wu
Publication date
1 July 2015
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments
Similar works
Full text
Available Versions
OPUS - University of Technology Sydney
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:opus.lib.uts.edu.au:10453/...
Last time updated on 13/02/2017
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1109%2Fijcnn.2015....
Last time updated on 03/08/2021