Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …

Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …

Brain activities can be evaluated by using Electroencephalogram (EEG) signals. One of the
primary reasons for traffic accidents is driver fatigue, which can be identified by using EEG …

Graph theory analysis, a mathematical approach, has been applied in brain connectivity
studies to explore the organization of network patterns. The computation of graph theory …

Electroencephalography (EEG) signals have been proven to be one of the most predictive
and reliable indicators for estimating driving fatigue state. However, how to make full use of …

Mental fatigue has shown to be one of the root causes of decreased productivity and overall
cognitive performance, by decreasing an individual's ability to inhibit responses, process …

The appearance of fatigue is not conducive to driving activities because this state can affect
driving performance and even cause life-threatening consequences. To reduce various …

In the nascent field of neuroergonomics, mental workload assessment is one of the most
important issues and has an apparent significance in real-world applications. Although prior …

Despite the apparent usefulness of efficient mental workload assessment in various real-
world situations, the underlying neural mechanism remains largely unknown, and studies of …

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 …