Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

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

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart,
digital, and connected industry, where well-being is key to enhance productivity, optimize …

Identifying the cognitive workload of operators is crucial in complex human-automation
collaboration systems. An excessive workload can lead to fatigue or accidents, while an …

Estimating cognitive workload levels is an emerging research topic in the cognitive
neuroscience domain, as participants' performance is highly influenced by cognitive …

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive
states to be able to provide help when it is needed and not overburden the human when the …

Assessing the cognitive workload of human interactants in mixed-initiative teams is a critical
capability for autonomous interactive systems to enable adaptations that improve team …

As a novel category of biometric features, research on brain fingerprints has become a hot
topic in neuroscience, not only for its reliable performance on individual identification but …

Traditional high-dimensional electroencephalography (EEG) features (spectral or temporal)
may not always attain satisfactory results in cognitive workload estimation. In contrast, deep …

Cognitive state assessment can be effectively performed using Electroencephalogram
(EEG). However, due to the curse of dimensionality issues of EEG, most of the clustering …