This paper presents a novel non-intrusive surrogate modeling scheme based on deep
learning for predictive modeling of complex systems, described by parametrized time …

Existing visual-based fatigue detection methods usually monitor drivers' fatigue by capturing
their facial features, including eyelid movements, yawn frequency and head pose. However …

Human status detection (HSD) is important to understand the status of users when
interacting with various systems under different conditions. Recently, although various …

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 …

Electroencephalogram (EEG) signals have been demonstrated to be an effective method for
game player expertise level classification, as it can reflect the activity state of the player's …

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 …

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 …

For deployment on an embedded processor for distracted driver classification, the model
should satisfy the demand for both high accuracy, real-time inference, and limited storage …

This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial
mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power …

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 …