EEG-Based Drivers Drowsiness Prediction Using Personalized Features Extraction and Classification Methods Under Python
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47672-3_16
Reference27 articles.
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2. Quddus, A., Shahidi Zandi, A., Prest, L., Comeau, F.J.E.: Using long short term memory and convolutional neural networks for driver drowsiness detection. Accid. Anal. Prev. 156(April), 106107 (2021). https://doi.org/10.1016/j.aap.2021.106107
3. Chen, L.L., Zhao, Y., Zhang, J., Zou, J.Z.: Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst. Appl. 42(21), 7344–7355 (2015). https://doi.org/10.1016/j.eswa.2015.05.028
4. Ouabida, E., Essadike, A., Bouzid, A.:. Optik Optical correlator based algorithm for driver drowsiness detection. Opt. Int. J. Light. Electron Opt. 204(December 2019), 164102 (2020). https://doi.org/10.1016/j.ijleo.2019.164102
5. Dhanalakshmi, S., Rosepet, J. J., Rosy, G. L., Philominal, M.: Drowsy driver identification using MATLAB 4(Iv), 198–205 (2016)
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