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A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model t...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755292/ https://www.ncbi.nlm.nih.gov/pubmed/31611912 http://dx.doi.org/10.1155/2019/3761203 |
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author | Zeng, Hong Yang, Chen Zhang, Hua Wu, Zhenhua Zhang, Jiaming Dai, Guojun Babiloni, Fabio Kong, Wanzeng |
author_facet | Zeng, Hong Yang, Chen Zhang, Hua Wu, Zhenhua Zhang, Jiaming Dai, Guojun Babiloni, Fabio Kong, Wanzeng |
author_sort | Zeng, Hong |
collection | PubMed |
description | Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI). |
format | Online Article Text |
id | pubmed-6755292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-67552922019-10-14 A LightGBM-Based EEG Analysis Method for Driver Mental States Classification Zeng, Hong Yang, Chen Zhang, Hua Wu, Zhenhua Zhang, Jiaming Dai, Guojun Babiloni, Fabio Kong, Wanzeng Comput Intell Neurosci Research Article Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI). Hindawi 2019-09-09 /pmc/articles/PMC6755292/ /pubmed/31611912 http://dx.doi.org/10.1155/2019/3761203 Text en Copyright © 2019 Hong Zeng et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zeng, Hong Yang, Chen Zhang, Hua Wu, Zhenhua Zhang, Jiaming Dai, Guojun Babiloni, Fabio Kong, Wanzeng A LightGBM-Based EEG Analysis Method for Driver Mental States Classification |
title | A LightGBM-Based EEG Analysis Method for Driver Mental States Classification |
title_full | A LightGBM-Based EEG Analysis Method for Driver Mental States Classification |
title_fullStr | A LightGBM-Based EEG Analysis Method for Driver Mental States Classification |
title_full_unstemmed | A LightGBM-Based EEG Analysis Method for Driver Mental States Classification |
title_short | A LightGBM-Based EEG Analysis Method for Driver Mental States Classification |
title_sort | lightgbm-based eeg analysis method for driver mental states classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755292/ https://www.ncbi.nlm.nih.gov/pubmed/31611912 http://dx.doi.org/10.1155/2019/3761203 |
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