A Cascaded Deep Forest Framework for Robust Driver Fatigue Detection using Forehead Electroencephalography.
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| Title: | A Cascaded Deep Forest Framework for Robust Driver Fatigue Detection using Forehead Electroencephalography. |
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| Authors: | YANG, Renyu1 25198850@qq.com, ZHANG, Ling2 258145593@qq.com, ZHONG, Boming3 1613462703@qq.com, HOU, Lixing3 382518926@qq.com, ZHU, Donglong4 2992357391@qq.com, MIN, Jianliang5,6 minjliang@mail2.sysu.edu.cn |
| Source: | Technical Gazette / Tehnički Vjesnik. 2025, Vol. 32 Issue 6, p2420-2427. 8p. |
| Subjects: | Electroencephalography, Biomedical signal processing, Wearable technology, Feature extraction, Traffic safety, Mental fatigue, Neurophysiology |
| Abstract: | Performance decrement due to fatigue is a leading contributor to traffic accidents and fatalities. Electroencephalogram (EEG) is widely accepted as a reliable physiological indicator of cognitive state, though the application of EEG-based systems in driver monitoring is often limited by the need for multichannel headsets. Forehead EEG, in particular, has emerged as a promising candidate for early detection due to the rise of portable wearable devices. In this work, we propose a robust and efficient method for driver fatigue detection using forehead EEG signals. The approach employs a cascaded deep forest (CDF) framework, incorporating wavelet log-energy entropy and high-order component statistics to extract meaningful features from low-channel EEG signals. A comprehensive labelling protocol was conducted across 26 subjects to validate the method. The experimental results demonstrated a significant improvement in performance, achieving an average accuracy of 95.1%, which outperformed previous studies. Furthermore, the energy characteristics of small-scale oscillations in brain signals across different frequency bands, along with the application of higherorder statistics in the reconstructed phase space, were validated for computational efficiency. This study presented a new framework of using frontal EEG based on a cascade structure to construct a landing fatigue detection method. It could also provide a promising approach for biomedical signal processing in low-channel systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 190580254 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Cascaded Deep Forest Framework for Robust Driver Fatigue Detection using Forehead Electroencephalography. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22YANG%2C+Renyu%22">YANG, Renyu</searchLink><relatesTo>1</relatesTo><i> 25198850@qq.com</i><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+Ling%22">ZHANG, Ling</searchLink><relatesTo>2</relatesTo><i> 258145593@qq.com</i><br /><searchLink fieldCode="AR" term="%22ZHONG%2C+Boming%22">ZHONG, Boming</searchLink><relatesTo>3</relatesTo><i> 1613462703@qq.com</i><br /><searchLink fieldCode="AR" term="%22HOU%2C+Lixing%22">HOU, Lixing</searchLink><relatesTo>3</relatesTo><i> 382518926@qq.com</i><br /><searchLink fieldCode="AR" term="%22ZHU%2C+Donglong%22">ZHU, Donglong</searchLink><relatesTo>4</relatesTo><i> 2992357391@qq.com</i><br /><searchLink fieldCode="AR" term="%22MIN%2C+Jianliang%22">MIN, Jianliang</searchLink><relatesTo>5,6</relatesTo><i> minjliang@mail2.sysu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2025, Vol. 32 Issue 6, p2420-2427. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+signal+processing%22">Biomedical signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+fatigue%22">Mental fatigue</searchLink><br /><searchLink fieldCode="DE" term="%22Neurophysiology%22">Neurophysiology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Performance decrement due to fatigue is a leading contributor to traffic accidents and fatalities. Electroencephalogram (EEG) is widely accepted as a reliable physiological indicator of cognitive state, though the application of EEG-based systems in driver monitoring is often limited by the need for multichannel headsets. Forehead EEG, in particular, has emerged as a promising candidate for early detection due to the rise of portable wearable devices. In this work, we propose a robust and efficient method for driver fatigue detection using forehead EEG signals. The approach employs a cascaded deep forest (CDF) framework, incorporating wavelet log-energy entropy and high-order component statistics to extract meaningful features from low-channel EEG signals. A comprehensive labelling protocol was conducted across 26 subjects to validate the method. The experimental results demonstrated a significant improvement in performance, achieving an average accuracy of 95.1%, which outperformed previous studies. Furthermore, the energy characteristics of small-scale oscillations in brain signals across different frequency bands, along with the application of higherorder statistics in the reconstructed phase space, were validated for computational efficiency. This study presented a new framework of using frontal EEG based on a cascade structure to construct a landing fatigue detection method. It could also provide a promising approach for biomedical signal processing in low-channel systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20250821002918 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 2420 Subjects: – SubjectFull: Electroencephalography Type: general – SubjectFull: Biomedical signal processing Type: general – SubjectFull: Wearable technology Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Traffic safety Type: general – SubjectFull: Mental fatigue Type: general – SubjectFull: Neurophysiology Type: general Titles: – TitleFull: A Cascaded Deep Forest Framework for Robust Driver Fatigue Detection using Forehead Electroencephalography. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: YANG, Renyu – PersonEntity: Name: NameFull: ZHANG, Ling – PersonEntity: Name: NameFull: ZHONG, Boming – PersonEntity: Name: NameFull: HOU, Lixing – PersonEntity: Name: NameFull: ZHU, Donglong – PersonEntity: Name: NameFull: MIN, Jianliang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 32 – Type: issue Value: 6 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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