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A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Joint Authors
Babiloni, Fabio
Kong, Wanzeng
Zeng, Hong
Yang, Chen
Zhang, Hua
Wu, Zhenhua
Zhang, Jiaming
Dai, Guojun
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-09-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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).
American Psychological Association (APA)
Zeng, Hong& Yang, Chen& Zhang, Hua& Wu, Zhenhua& Zhang, Jiaming& Dai, Guojun…[et al.]. 2019. A LightGBM-Based EEG Analysis Method for Driver Mental States Classification. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1129430
Modern Language Association (MLA)
Zeng, Hong…[et al.]. A LightGBM-Based EEG Analysis Method for Driver Mental States Classification. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1129430
American Medical Association (AMA)
Zeng, Hong& Yang, Chen& Zhang, Hua& Wu, Zhenhua& Zhang, Jiaming& Dai, Guojun…[et al.]. A LightGBM-Based EEG Analysis Method for Driver Mental States Classification. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1129430
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129430