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Innovative deep learning models for EEG-based vigilance detection

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A Correction to this article was published on 18 June 2021

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Abstract

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 the subject vigilance states and compete with the state of art on multiple performance metrics. The per-class average of precision and recall can be, respectively, up to 86% with 1D-UNet and 85% with 1D-UNet-LSTM, hence the effectiveness of the proposed methods. In order to complete our virtual prototyping and to get a real evaluation of our alert equipment, these proposed DL models are implemented also on a Raspberry Pi3 device allowing measuring the execution time necessary for predicting the state vigilance in real time.

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Authors and Affiliations

  1. Laboratoire de Technologie et Imagerie Médicale, Faculté de Médecine de Monastir, Université de Monastir, 5019, Monastir, Tunisia

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

  2. Institut Supérieur des Sciences Appliquées et de Technologie de Sousse, Université de Sousse, 4003, Sousse, Tunisia

    Ahmed Ghazi Blaiech & Khaled Ben Khalifa

  3. Institut supérieur d’informatique et de Mathématiques, Université de Monastir, 5019, Monastir, Tunisia

    Asma Ben Abdallah

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  1. Souhir Khessiba
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  2. Ahmed Ghazi Blaiech
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Correspondence to Ahmed Ghazi Blaiech.

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Khessiba, S., Blaiech, A.G., Ben Khalifa, K. et al. Innovative deep learning models for EEG-based vigilance detection. Neural Comput & Applic 33, 6921–6937 (2021). https://doi.org/10.1007/s00521-020-05467-5

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