Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …

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 …

Mental workload assessment is essential for maintaining human health and preventing
accidents. Most research on this issue is limited to a single task. However, cross-task …

Mental workload refers to the cognitive effort required to perform tasks, and it is an important
factor in various fields, including system design, clinical medicine, and industrial …

The accurate evaluation of operators' mental workload in human-machine systems plays an
important role in ensuring the correct execution of tasks and the safety of operators …

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

Despite the apparent importance of mental fatigue detection, a reliable application is
hindered due to the incomprehensive understanding of the neural mechanisms of mental …

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 …

Regarding cognitive workload recognition (CWR), electroencephalography (EEG) signals
are nonstationary across time and vary from different subjects, thus hindering the cross …

Cognitive workload recognition is pivotal to maintain the operator's health and prevent
accidents in the human-robot interaction condition. So far, the focus of workload research is …