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• W1992900302 abstract "Drowsiness is the main factors in traffic accidents because the ability of vehicle driver was diminished. These conditions will endanger to own driver and the other vehicle drivers. With the growing traffic conditions this problem will increase in the future. So, it is important to develop automatic characterization of the drowsiness stage. The aim of this paper presents a new method to improve wavelet coefficient of DWT for classification alert and drowsiness stages of EEG signals. The method applied the Parseval's theorem and energy coefficient distribution. The Input-Output cluster method was used to estimate the approximate status of each input features. Then these improve features are feeded into neural network classifier. The proposed method gets 90.27% of accuracy. The experimental results have shown that the proposed approach can achieve better performance in comparison with other based methods." @default.
• W1992900302 created "2016-06-24" @default.
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• W1992900302 date "2014-12-01" @default.
• W1992900302 modified "2023-09-26" @default.
• W1992900302 title "Classification of Drowsiness in EEG Records Based on Energy Distribution and Wavelet-Neural Network" @default.
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• W1992900302 doi "https://doi.org/10.1109/cse.2014.306" @default.
• W1992900302 hasPublicationYear "2014" @default.
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