標題: Single Channel Wireless EEG Device for Real-Time Fatigue Level Detection
作者: Ko, Li-Wei
Lai, Wei-Kai
Liang, Wei-Gang
Chuang, Chun-Hsiang
Lu, Shao-Wei
Lu, Yi-Chen
Hsiung, Tien-Yang
Wu, Hsu-Hsuan
Lin, Chin-Teng
生物資訊及系統生物研究所
腦科學研究中心
Institude of Bioinformatics and Systems Biology
Brain Research Center
關鍵字: driver drowsiness detection;Brain computer interface;wearable devices
公開日期: 2015
摘要: 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.
URI: http://hdl.handle.net/11536/134635
ISBN: 978-1-4799-1959-8
ISSN: 2161-4393
期刊: 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
顯示於類別:會議論文