Physiological computing uses human physiological data as system inputs in real time. It
includes, or significantly overlaps with, brain-computer interfaces, affective computing …

Medical IoT devices are rapidly becoming part of management ecosystems for pandemics
such as COVID-19. Existing research shows that deep learning (DL) algorithms have been …

The discovery of adversarial examples has raised concerns about the practical deployment
of deep learning systems. In this paper, we demonstrate that adversarial examples are …

Deep learning (DL) has been widely employed in brain--machine interfaces (BMIs) to
decode subjects' intentions based on recorded brain activities enabling direct interaction …

Today, the security of wearable and mobile-health technologies represents one of the main
challenges in the Internet of Things (IoT) era. Adversarial manipulation of sensitive health …

Deep neural networks for medical images are extremely vulnerable to adversarial examples
(AEs), which poses security concerns on clinical decision-making. Recent findings have …

With the recent advancement of the Brain-Computer Interface (BCI), Electroencephalogram
(EEG) analytics gain a lot of research attention from various domains. Understanding the …

Adversarial attacks are considered a potentially serious security threat for machine learning
systems. Medical image analysis (MedIA) systems have recently been argued to be …

Many machine learning approaches have been successfully applied to
electroencephalogram (EEG) based brain–computer interfaces (BCIs). Most existing …

The increasing availability of healthcare data requires accurate analysis of disease
diagnosis, progression, and real-time monitoring to provide improved treatments to the …