Innovative deep learning models for EEG-based vigilance detection
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ORIGINAL ARTICLE
Innovative deep learning models for EEG-based vigilance detection Souhir Khessiba1 • Ahmed Ghazi Blaiech1,2 Mohamed He´di Bedoui1
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Khaled Ben Khalifa1,2 • Asma Ben Abdallah1,3
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Received: 21 April 2020 / Accepted: 26 October 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Vigilance Deep learning EEG signal Classification
1 Introduction Artificial neural networks (ANNs) have achieved a great resurgence during the past few years in explicitly explaining decisions or actions to a human observer. In fact, ANNs use the processing of the brain as a basis to develop algorithms that can be utilized to model complex patterns and prediction problems. Feature extraction is a
& Ahmed Ghazi Blaiech [email protected] 1
Laboratoire de Technologie et Imagerie Me´dicale, Faculte´ de Me´decine de Monastir, Universite´ de Monastir, 5019 Monastir, Tunisia
2
Institut Supe´rieur des Sciences Applique´es et de Technologie de Sousse, Universite´ de Sousse, 4003 Sousse, Tunisia
3
Institut supe´rieur d’informatique et de Mathe´matiques, Universite´ de Monastir, 5019 Monastir, Tunisia
fundamental step by which an initial set of data is reduced by identifying key features of these data. However, deep learning (
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