Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular
potentials generated when the brain instructs the body to perform both fine and coarse …

Electroencephalography (EEG) is a medical engineering technique aimed at recording the
electric activity of the human brain. Brain signals derived from an EEG device can be …

Objective: To evaluate the impact on Electroencephalography (EEG) classification of
different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We …

Delivering health care at home emerged as a key advancement to reduce healthcare costs
and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training …

Electroencephalografic (EEG) data are complex multi-dimensional time-series which are
very useful in many different applications, ie, from diagnostics of epilepsy to driving brain …

Dealing with a large amount of data becomes a major challenge in data mining and
machine learning. Feature selection is a significant preprocessing step for selecting the most …

In this study, we propose a Binary Particle Swarm Optimization algorithm hybridizing with
Oppositionbased Learning for solving the feature selection problem. Opposition-based …

As the worldwide population gets increasingly aged, in-home telemedicine and mobile-
health solutions represent promising services to promote active and independent aging and …

In the past few decades, the large amount of available data has become a major challenge
in data mining and machine learning. Feature selection is a significant preprocessing step …

Recent evidence has revealed cross-frequency coupling and, particularly, phase-amplitude
coupling (PAC) as an important strategy for the brain to accomplish a variety of high-level …