id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-027732-8i8bwlh8	Boudaya, Amal	EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network	2020-05-31		.txt	text/plain	2337	148	49	Given, its high temporal resolution, portability and reasonable cost, the present work focus on hypo-vigilance detection by analyzing EEG signal of various brain's functionalities using fourteen electrodes placed on the participant's scalp. On the other hand, deep learning networks offer great potential for biomedical signals analysis through the simplification of raw input signals (i.e., through various steps including feature extraction, denoising and feature selection) and the improvement of the classification results. In this paper, we focus on the EEG signal study recorded by fourteen electrodes for hypo-vigilance detection by analyzing the various functionalities of the brain from the electrodes placed on the participant's scalp. In this paper, we propose a CNN hypo-vigilance detection method using EEG data in order to classify drowsiness and awakeness states. In the proposed simple CNN architecture for EEG signals classification, we use the Keras deep learning library.	./cache/cord-027732-8i8bwlh8.txt	./txt/cord-027732-8i8bwlh8.txt