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http://dx.doi.org/10.7840/kics.2014.39C.10.887

Electroencephalogram-Based Driver Drowsiness Detection System Using Errors-In-Variables(EIV) and Multilayer Perceptron(MLP)  

Han, Hyungseob (School of Electrical Engineering, University of Ulsan)
Song, Kyoung-Young (School of Electrical Engineering, Ulsan College)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.39C, no.10, 2014 , pp. 887-895 More about this Journal
Abstract
Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.
Keywords
Auto-regressive(AR) model; drowsiness detection; electroencephalogram(EEG); errors-in-variables(EIV); multilayer perceptron(MLP);
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Times Cited By KSCI : 5  (Citation Analysis)
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