Single channel wireless EEG device for real-time fatigue level detection. Paper presented at the 2015 International Joint Conference on Neural Networks IJCNN, Killarney, Ireland, July 11-16, 2015.

Author(s)
Ko, L.-W. Lai, W.-K. Liang, W.-G. Chuang, C.-H. Lu, S.-W. Lu, Y.-C. Hsiung, T.-Y. Wu, H.-H. & Lin, C.-T.
Year
Abstract

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. (Author/publisher).

Publication

Library number
20210300 ST [electronic version only]
Source

In: Proceedings of the 2015 International Joint Conference on Neural Networks IJCNN, Killarney, Ireland, July 11-16, 2015, 5 p., 14 ref.

Our collection

This publication is one of our other publications, and part of our extensive collection of road safety literature, that also includes the SWOV publications.