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 |