Electroencephalography (EEG) is immediate and sensitive to neurological changes
resulting from sleep stages and is considered a computing tool for understanding the …

The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …

Study objectives Development of inter-database generalizable sleep staging algorithms
represents a challenge due to increased data variability across different datasets. Sharing …

Automatic sleep staging has been an active field of development. Despite multiple efforts,
the area remains a focus of research interest. Indeed, while promising results have reported …

Sleep staging has always been a hot topic in the field of sleep medicine, and it is the
cornerstone of research on sleep problems. At present, sleep staging heavily relies on …

Since the release of the contrastive language-image pre-training (CLIP) model designed by
the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep …

As sleep disorders are becoming overwhelmingly prevalent in industrialized countries, a
simple, readily accessible sleep stage classification technology is strikingly at need …

The growing demand for semi-autonomous human–machine systems has led to an
increased requirement for human fatigue detection. Direct and invasive approaches for …

Classification of sleep stages is one of the most important diagnostic approaches for a
variety of sleep-related disorders. Electroencephalography (EEG) is regarded as a powerful …

Brain states (wake, sleep, general anesthesia, etc.) are profoundly associated with the
spatiotemporal dynamics of brain oscillations. Previous studies showed that the EEG alpha …