@article{ref1,
title="A portable device for real time drowsiness detection using novel active dry electrode system",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2009",
author="Tsai, Pai-Yuan and Hu, Weichih and Kuo, Terry B. J. and Shyu, Liang-Yu",
volume="2009",
number="",
pages="3775-3778",
abstract="Electroencephalogram (EEG) signals give important information about the vigilance states of a subject. Therefore, this study constructs a real-time EEG-based system for detecting a drowsy driver. The proposed system uses a novel six channels active dry electrode system to acquire EEG non-invasively. In addition, it uses a TMS320VC5510 DSP chip as the algorithm processor, and a MSP430F149 chip as a controller to achieve a real-time portable system. This study implements stationary wavelet transform to extract two features of EEG signal: integral of EEG and zero crossings as the input to a back propagation neural network for vigilance states classification. This system can discriminate alertness and drowsiness in real-time. The accuracy of the system is 79.1% for alertness and 90.91% for drowsiness states. When the system detects drowsiness, it will warn drivers by using a vibrator and a beeper.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/IEMBS.2009.5334491",
url="http://dx.doi.org/10.1109/IEMBS.2009.5334491"
}