Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as
engineering applications. However, one of the challenges associated with recording EEG …

Objective The proper handling of missing values is critical to delivering reliable estimates
and decisions, especially in high-stakes fields such as clinical research. In response to the …

In this paper, we present a systematic literature review on deep generative models for
physiological signals, particularly electrocardiogram, electroencephalogram …

High-precision acquisition of dense-channel electroencephalogram (EEG) signals is often
impeded by the costliness and lack of portability of equipment. In contrast, generating dense …

Sleep abnormalities can have severe health consequences. Automated sleep staging, ie
labelling the sequence of sleep stages from the patient's physiological recordings, could …

In disease detection, generative models for data augmentation offer a potential solution to
the challenges posed by limited high-quality electroencephalogram (EEG) data. The study …

Skin-based infections are one of the primary causes of the global disease burden. Digital
Health Technologies powered by data science models have the potential to revolutionize …

Aiming at enhancing classification performance and improving user experience of a brain-
computer interface (BCI) system, this paper proposes an improved Wasserstein generative …

Recent advances in machine learning have led to revolutionary breakthroughs in computer
games, image and natural language understanding, and scientific discovery. Foundation …

Data quality plays a crucial role in tasks, such as enhancing the accuracy of data analytics
and avoiding the accumulation of redundant data. One of the significant challenges in data …