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Innovative deep learning models for EEG-based vigilance detection by Khessiba, Souhir; Blaiech, Ahmed Ghazi; Ben Khalifa, Khaled; Ben Abdallah, Asma; Bedoui, Mohamed Hédi is a Computer Science article available to read on EtoBox.

What is Innovative deep learning models for EEG-based vigilance detection about?

Electroencephalography (EEG) is one of the most signals used for studying and demonstrating the electrical activity of the brain due to the absence of side effects, its noninvasive nature and its well temporal resolution. Indeed, it provides real-time information, so it can be easily suitable for predicting drivers' vigilance states. The classification of these states through this signal requires sophisticated approaches in order to achieve the best prediction performance. Furthermore, deep learning (DL) approaches have shown a good performance in learning the high-level features of the EEG signal and in resolving classification issues. In this paper, we will predict individuals' states of vigilance based on the study of their brain activity by analyzing EEG signals using DL architectures. In fact, we propose two types of networks: (i) a 1D-UNet model, which is composed only of deep one-dimensional convolutional neural network (1D-CNN) layers and (ii) 1D-UNet-long short-term memory (1D-UNet-LSTM) that combines the proposed 1D-UNet architecture with the LSTM recurrent model. The experimental results reveal that the suggested models can stabilize the training model, well recognize th

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Author

Khessiba, Souhir; Blaiech, Ahmed Ghazi; Ben Khalifa, Khaled; Ben Abdallah, Asma; Bedoui, Mohamed Hédi

Publisher

Springer-Verlag; Springer Verlag; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 0941-0643)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)