A Cascaded Deep Forest Framework for Robust Driver Fatigue Detection using Forehead Electroencephalography.

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Bibliographic Details
Title:	A Cascaded Deep Forest Framework for Robust Driver Fatigue Detection using Forehead Electroencephalography.
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, Jianliang^5,6 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]
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Database:	Engineering Source

Description
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]
ISSN:	13303651
DOI:	10.17559/TV-20250821002918