The stope dynamic disaster, which occurs around the mining space and is represented by
stress-type rock burst and fracture-type rock burst, seriously affects the safety production of …

This article summarizes a systematic literature review of deep neural network-based
cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The …

Neurodegenerative diseases (NDs), such as Alzheimer's, Parkinson's, amyotrophic lateral
sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the …

The human brain is perpetually active, operating during both work and rest. Excessive
mental activity, termed overload, can detrimentally impact health. Advances in predicting …

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately
measure the cognitive demands placed on an individual during multitasking mental …

Background: The integration of advanced technologies such as augmented reality (AR) and
virtual reality (VR) into surgical procedures has garnered significant attention. However, the …

The instantaneous frequency measurement is the primary focus of different variably-
dimensions signal processing applications. It addresses the non-stationarity of signals …

The characterization of peripheral myelinated fibers through in-vitro or in-vivo
electrophysiological experiments provides valuable insights into the functional aspects of …

Electrical signals generated in the brain, known as Electroencephalographic signals (EEG)
are used to measure electrical activities in the brain. EEG methods being non-invasive …

Cognitive workload is a widely used concept for addressing the balance between task
demand and the subject's available resources to complete the task. Electrophysiological …