CNN-LSTM To Identify The Most Informative EEG-Based Driver Drowsiness Detection Brain Region

As driver drowsiness causes dangerous results such as accidents and deaths, it is crucial to develop a system that can detect it in its early stage and precisely. Numerous studies employed various methods to detect drowsiness; however, Electroencephalogram (EEG) signals were the most accurate. A dri...

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Bibliographic Details

Published in:	2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) pp. 725 - 730
Main Authors:	Latreche, Imene, Slatnia, Sihem, Kazar, Okba
Format:	Conference Proceeding
Language:	English
Published:	IEEE 20-10-2022
Subjects:	Brain modeling Brain regions CNN Costs Deep learning Driver Drowsiness Electrodes Electroencephalogram (EEG) LSTM Scalp Signal processing Wearable computers
Online Access:	Get full text
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