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http://dx.doi.org/10.7840/kics.2015.40.9.1731

Improvement of EEG-Based Drowsiness Detection System Using Discrete Wavelet Transform  

Han, Hyungseob (School of Electrical Engineering, University of Ulsan)
Song, Kyoung-Young (School of Electrical Engineering, University of Ulsan)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.40, no.9, 2015 , pp. 1731-1733 More about this Journal
Abstract
Since electroencephalogram(EEG) has non-linear and non-stationary properties, it is effective to analyze the characteristic of EEG with time-frequency method rather than spectrum method. In this letter, we propose the modified drowsiness detection system using discrete wavelet transform combined with errors-in-variables and multilayer perceptron methods. For the comparison of the proposed scheme with the previous one, the state 'others' is added to the previous states of drivers: 'alertness,' 'transition,' and 'drowsiness.' From the computer simulation using machine learning, we confirm that the proposed scheme outperforms the previous one for some conditions.
Keywords
discrete wavelet transform (DWT); drowsiness detection; electroencephalogram (EEG); errors-in-variables (EIV);
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Times Cited By KSCI : 1  (Citation Analysis)
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