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 …

In this article, we propose a method for emotion classification based on multilayer
convolutional neural network (MCNN) and combining differential entropy (DE) and brain …

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 …

Abstract Autonomous-rail Rapid Transit (ART) is a new type of road public transportation
with an automatic driving function, and it is one of the main places for epidemic spread …

Driving fatigue detection based on EEG signals is a research hotspot in applying brain-
computer interfaces. EEG signal is complex, unstable, and nonlinear. Most existing methods …

Chemotherapy optimization based on mathematical models is a promising direction of
personalized medicine. Personalizing, thus optimizing treatments, may have multiple …

Driving fatigue is a common experience for most drivers and can reduce human cognition
and judgment abilities. Previous studies have exhibited a phenomenon of the non …

Electroencephalography (EEG) data serve as a reliable method for fatigue detection due to
their intuitive representation of drivers' mental processes. However, existing research on …

Epilepsy makes the patients suffer great pain and has a very bad impact on daily life. In this
paper, a novel method is proposed to implement electroencephalogram (EEG)-based …

Human emotions are an important part in daily life. In this paper, a novel multilayer network-
based convolutional neural network (CNN) model is proposed for emotion recognition, from …