Detection of the pilots' mental states is particularly critical because their abnormal mental
states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility …

Automated source separation algorithms have become a central tool in neuroengineering
and neuroscience, where they are used to decompose neurophysiological signal into its …

Speech imagery-based Brain-computer interface (BCI) using Electroencephalogram (EEG)
signal is a promising area of research for individuals with severe speech production …

Numerous individuals encounter challenges in verbal communication due to various factors,
including physical disabilities, neurological disorders, and strokes. In response to this …

Background Cardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical
research has shown that early identification of CA reduces mortality. Algorithms capable of …

The global prevalence of childhood and adolescent obesity is a major concern due to its
association with chronic diseases and long-term health risks. Artificial intelligence …

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the
understanding of how the brain encodes semantic information and contribute to semantic …

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response
to external events and their associated underlying complex spatiotemporal feature …

Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record
discrete signals across multiple scales, but how to effectively process and analyze these …

Mind-uploading, the vision of transferring human consciousness into a digital realm, relies
on a profound comprehension of the brain and cutting-edge technology. Non-invasive …