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EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks
Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiol...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033857/ https://www.ncbi.nlm.nih.gov/pubmed/36968502 http://dx.doi.org/10.3389/fnins.2023.1136609 |
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author | Gao, Dongrui Tang, Xue Wan, Manqing Huang, Guo Zhang, Yongqing |
author_facet | Gao, Dongrui Tang, Xue Wan, Manqing Huang, Guo Zhang, Yongqing |
author_sort | Gao, Dongrui |
collection | PubMed |
description | Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiological signals. Still, lighting and the environment affect the detection results based on facial expressions. In contrast, the electroencephalographic (EEG) signal is a physiological signal that directly responds to the human mental state, thus reducing the impact on the detection results. This paper proposes a log-Mel spectrogram and Convolution Recurrent Neural Network (CRNN) model based on EEG to implement driver fatigue detection. This structure allows the advantages of the different networks to be exploited to overcome the disadvantages of using them individually. The process is as follows: first, the original EEG signal is subjected to a one-dimensional convolution method to achieve a Short Time Fourier Transform (STFT) and passed through a Mel filter bank to obtain a logarithmic Mel spectrogram, and then the resulting logarithmic Mel spectrogram is fed into a fatigue detection model to complete the fatigue detection task for the EEG signals. The fatigue detection model consists of a 6-layer convolutional neural network (CNN), bi-directional recurrent neural networks (Bi-RNNs), and a classifier. In the modeling phase, spectrogram features are transported to the 6-layer CNN to automatically learn high-level features, thereby extracting temporal features in the bi-directional RNN to obtain spectrogram-temporal information. Finally, the alert or fatigue state is obtained by a classifier consisting of a fully connected layer, a ReLU activation function, and a softmax function. Experiments were conducted on publicly available datasets in this study. The results show that the method can accurately distinguish between alert and fatigue states with high stability. In addition, the performance of four existing methods was compared with the results of the proposed method, all of which showed that the proposed method could achieve the best results so far. |
format | Online Article Text |
id | pubmed-10033857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100338572023-03-24 EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks Gao, Dongrui Tang, Xue Wan, Manqing Huang, Guo Zhang, Yongqing Front Neurosci Neuroscience Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiological signals. Still, lighting and the environment affect the detection results based on facial expressions. In contrast, the electroencephalographic (EEG) signal is a physiological signal that directly responds to the human mental state, thus reducing the impact on the detection results. This paper proposes a log-Mel spectrogram and Convolution Recurrent Neural Network (CRNN) model based on EEG to implement driver fatigue detection. This structure allows the advantages of the different networks to be exploited to overcome the disadvantages of using them individually. The process is as follows: first, the original EEG signal is subjected to a one-dimensional convolution method to achieve a Short Time Fourier Transform (STFT) and passed through a Mel filter bank to obtain a logarithmic Mel spectrogram, and then the resulting logarithmic Mel spectrogram is fed into a fatigue detection model to complete the fatigue detection task for the EEG signals. The fatigue detection model consists of a 6-layer convolutional neural network (CNN), bi-directional recurrent neural networks (Bi-RNNs), and a classifier. In the modeling phase, spectrogram features are transported to the 6-layer CNN to automatically learn high-level features, thereby extracting temporal features in the bi-directional RNN to obtain spectrogram-temporal information. Finally, the alert or fatigue state is obtained by a classifier consisting of a fully connected layer, a ReLU activation function, and a softmax function. Experiments were conducted on publicly available datasets in this study. The results show that the method can accurately distinguish between alert and fatigue states with high stability. In addition, the performance of four existing methods was compared with the results of the proposed method, all of which showed that the proposed method could achieve the best results so far. Frontiers Media S.A. 2023-03-09 /pmc/articles/PMC10033857/ /pubmed/36968502 http://dx.doi.org/10.3389/fnins.2023.1136609 Text en Copyright © 2023 Gao, Tang, Wan, Huang and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Gao, Dongrui Tang, Xue Wan, Manqing Huang, Guo Zhang, Yongqing EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks |
title | EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks |
title_full | EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks |
title_fullStr | EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks |
title_full_unstemmed | EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks |
title_short | EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks |
title_sort | eeg driving fatigue detection based on log-mel spectrogram and convolutional recurrent neural networks |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033857/ https://www.ncbi.nlm.nih.gov/pubmed/36968502 http://dx.doi.org/10.3389/fnins.2023.1136609 |
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