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

Emotion is a subjective psychophysiological reaction coming from external stimuli which
impacts every aspect of our daily lives. Due to the continuing development of non-invasive …

The use of cloud computing service models is rapidly increasing, but inefficient resource
usage in cloud data centers can lead to great energy consumption and costs. To address …

In the field of brain–computer interface (BCI), brain decoding using electroencephalography
(EEG) is an essential direction, and motion imagery EEG-based BCI can not only help …

Introduction Major depressive disorder (MDD) is the most common mental disorder
worldwide, leading to impairment in quality and independence of life …

Affect decoding through brain-computer interfacing (BCI) holds great potential to capture
users' feelings and emotional responses via non-invasive electroencephalogram (EEG) …

The inflow of cyber-attacks on the services of critical information infrastructure (CII) has
necessitated adequate attention to their security, functionality and continuous existence …

The integration of emotional intelligence in machines is an important step in advancing
human–computer interaction. This demands the development of reliable end-to-end emotion …

Electroencephalogram (EEG)-based emotion recognition has demonstrated encouraging
results using machine learning (ML)-based algorithms. This study compares the …

Machine learning (ML) is a popular tool in healthcare while it can help to analyze large
amounts of patient data, such as medical records, predict diseases, and identify early signs …