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Graph neural network-driven traffic forecasting for the connected internet of vehicles
Due to great advances in wireless communication, the connected Internet of vehicles
(CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an …
(CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an …
EEG-Based driver Fatigue Detection using Spatio-Temporal Fusion network with brain region partitioning strategy
Detecting driver fatigue is critical for ensuring traffic safety. Electroencephalography (EEG) is
the golden standard for brain activity measurement and is considered a good indicator of …
the golden standard for brain activity measurement and is considered a good indicator of …
Lggnet: Learning from local-global-graph representations for brain–computer interface
Neuropsychological studies suggest that co-operative activities among different brain
functional areas drive high-level cognitive processes. To learn the brain activities within and …
functional areas drive high-level cognitive processes. To learn the brain activities within and …
CSF-GTNet: A novel multi-dimensional feature fusion network based on Convnext-GeLU-BiLSTM for EEG-signals-enabled fatigue driving detection
D Gao, P Li, M Wang, Y Liang, S Liu… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Electroencephalography (EEG) signal has been recognized as an effective fatigue detection
method, which can intuitively reflect the drivers' mental state. However, the research on multi …
method, which can intuitively reflect the drivers' mental state. However, the research on multi …
[HTML][HTML] Designing a practical fatigue detection system: A review on recent developments and challenges
Introduction Fatigue is considered to have a life-threatening effect on human health and it
has been an active field of research in different sectors. Deploying wearable physiological …
has been an active field of research in different sectors. Deploying wearable physiological …
Self-attentive channel-connectivity capsule network for EEG-based driving fatigue detection
Deep neural networks have recently been successfully extended to EEG-based driving
fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel …
fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel …
Driver drowsiness detection methods using EEG signals: a systematic review
Electroencephalography (EEG) is a complex signal that may require several years of
training, advanced signal processing, and feature extraction methodologies to interpret …
training, advanced signal processing, and feature extraction methodologies to interpret …
Rail transit obstacle detection based on improved CNN
D He, Z Zou, Y Chen, B Liu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With the continuous development of rail transit fully automatic operation, the urgent need to
improve train operation safety makes obstacle detection become the research focus. In this …
improve train operation safety makes obstacle detection become the research focus. In this …
An EEG-based brain cognitive dynamic recognition network for representations of brain fatigue
Fatigue driving will seriously threaten public safety and health, so monitoring the brain's
cognitive state accurately and exploring the fatigue process is essential. This paper …
cognitive state accurately and exploring the fatigue process is essential. This paper …
Wearable supernumerary robotic limb system using a hybrid control approach based on motor imagery and object detection
Motor disorder of upper limbs has seriously affected the daily life of the patients with
hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) …
hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) …