Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and β) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue.
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http://dx.doi.org/10.1016/j.neulet.2011.11.014 | DOI Listing |
Background: Critical care nurses are vulnerable to depression, which not only lead to poor well-being and increased turnover intention, but also affect their working performances and organizational productivity as well. Work related factors are important drivers of depressive symptoms. However, the non-liner and multi-directional relationships between job demands-resources and depressive symptoms in critical care nurses has not been adequately analyzed.
View Article and Find Full Text PDFSci Rep
January 2025
College of Smart City and Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China.
Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers' mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Republic of Korea.
Unlabelled: This study aims to create a fatigue recognition system that utilizes electroencephalogram (EEG) signals to assess a driver's physiological and mental state, with the goal of minimizing the risk of road accidents by detecting driver fatigue regardless of physical cues or vehicle attributes. A fatigue state recognition system was developed using transfer learning applied to partial ensemble averaged EEG power spectral density (PSD). The study utilized layer-wise relevance propagation (LRP) analysis to identify critical cortical regions and frequency bands for effective fatigue discrimination.
View Article and Find Full Text PDFRecent Pat Anticancer Drug Discov
January 2025
Department of Medical Oncology, The Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210009, China.
Objective: This study aimed to explore the clinical efficacy and safety of durvalumab combined with albumin-bound paclitaxel and carboplatin as neoadjuvant therapy for resectable stage III Non-small Cell Lung Cancer (NSCLC).
Methods: A single-arm open-label phase Ib study was conducted. A total of 40 patients with driver gene-negative resectable stage III NSCLC were enrolled.
Sensors (Basel)
December 2024
Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.
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