Technical gazette, Vol. 32 No. 6, 2025.
Original scientific paper
https://doi.org/10.17559/TV-20250821002918
A Cascaded Deep Forest Framework for Robust Driver Fatigue Detection using Forehead Electroencephalography
Renyu Yang
; Guangdong University of Finance & Economics, Guangzhou 510320, China
Ling Zhang
; Guangzhou Vocational College of Technology & Business, Guangzhou 511442, China
Boming Zhong
; Guangdong University of Finance & Economics, Guangzhou 510320, China
Lixing Hou
; Guangdong University of Finance & Economics, Guangzhou 510320, China
Donglong Zhu
; Guangdong University of Finance & Economics, Guangzhou 510320, China
Jianliang Min
; Organ Transplantation Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China; School of Medicine, Jiaying University, Meizhou 514015, China
*
* Corresponding author.
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 higher-order 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.
Keywords
driving fatigue; deep forest; forehead EEG; traffic safety; wearable device
Hrčak ID:
337744
URI
Publication date:
31.10.2025.
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