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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...

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Detalles Bibliográficos
Autores principales: Zeng, Hong, Yang, Chen, Zhang, Hua, Wu, Zhenhua, Zhang, Jiaming, Dai, Guojun, Babiloni, Fabio, Kong, Wanzeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
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).
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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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