@article{ref1,
title="Estimating vigilance in driving simulation using probabilistic PCA",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2008",
author="Li, Mingqiang and Fu, Jia-Wei and Lu, Bao-Liang",
volume="2008",
number="",
pages="5000-5003",
abstract="In avoiding fatal consequences in accidents behind steering wheel caused by low level vigilance, EEG has shown bright prospects. In this paper, we propose a novel method for discriminating two different vigilance states of the subjects, namely wake state and sleep state, during driving a car in a simulation environment. After filtering the EEG data into a specific frequency band, we use probabilistic principle component analysis (PPCA) to reduce the data dimension. Then we model each vigilance state as a lower dimension Gaussian random variable by applying PPCA again. The feature related to class posterior probability is calculated for classification. The experimental results show satisfying time resolution (< or = 5s) and high accuracy (> or = 96%) across five subjects on both common frequency bands beta (19-26 Hz) and gamma (38-42 Hz), and a broad band (8-30 Hz).<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/IEMBS.2008.4650337",
url="http://dx.doi.org/10.1109/IEMBS.2008.4650337"
}