Aug 2026· Cognitive Neurodynamics· Vol 20· 0 citations· 44 references
Medicine
TL;DR
This study provides a computationally efficient framework with feature projections for multimodal EEG feature extraction in fatigue monitoring systems by integrating complementary connectivity views.
A lightweight Dual-Branch Multidimensional Attention Fusion Network (DB-MDAFNet) that integrates spectral-temporal features and PCC-derived inter-channel dependency representations for EEG-based fatigue classification provides a lightweight and interpretable architecture for EEG-based fatigue monitoring.
Jing-Qing Lu, Shi-Hong Liu, Wei Li et al.· Frontiers in Physiology· 0 citations
Electroencephalography (EEG)-based driver fatigue recognition remains challenging because fatigue-related patterns are distributed across multiple temporal scales, inter-channel relationships vary across samples, and inter-individual differences complicate cross-subject transfer. To address these issues, a multi-scale...
Pei-Pei Gu, Rui-Bo Wang, Ming Zhang et al.· Frontiers in Neuroscience· 0 citations
Driver fatigue is a leading contributor to fatal traffic accidents globally, and Electroencephalogram (EEG)-Electrooculogram (EOG) multi-modal systems are the gold standard for objective fatigue detection. However, existing Transformer-based methods face critical bottlenecks of high quadratic complexity and oversimplif...
Jiantao Yuan, Jingrong Tang, Hai-Jun Han et al.· IEEE journal of biomedical a...· 0 citations
Electroencephalography (EEG)-based Brain–Computer Interfaces offer direct insight into a driver’s mental state, relevant to autonomous-vehicle perception stacks. This work addresses vigilance/drowsiness specifically, one of several driver states relevant to Level 3 takeover readiness. Detecting drowsiness before handov...
Sirine Ammar, M. Karray, Mohamed Ksantini· Electronics· 0 citations
Results indicate that the proposed method achieves a favorable balance between detection accuracy and model compactness under the present benchmark setting, and indicates that the proposed method achieves a favorable balance between detection accuracy and model compactness under the present benchmark setting.
Jia-Zheng Sun, Cai Chen, Li-Jun Liu et al.· IEEE Access· 0 citations
This structured critical review examines wearable brain–computer interface (BCI) systems that integrate electroencephalographic (EEG) and electromyographic (EMG) signals for post-stroke motor rehabilitation. The central engineering problem is the spatio-temporal heterogeneity between cortical and muscular signals, whic...