Stroke is one of the leading causes of long-term disability among adults and contributes to
major socio-economic burden globally. Stroke frequently results in multifaceted impairments …

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort
expended in the interface of medicine and computer science. As some CAD systems in …

Brain computer interface technology represents a highly growing field of research with
application systems. Its contributions in medical fields range from prevention to neuronal …

Strokes are a growing cause of mortality and many stroke survivors suffer from motor
impairment as well as other types of disabilities in their daily life activities. To treat these …

Abstract Machine learning techniques are increasingly used in neuroscience to classify
brain signals. Decoding performance is reflected by how much the classification results …

This document provides a review of the techniques and therapies used in gait rehabilitation
after stroke. It also examines the possible benefits of including assistive robotic devices and …

Stroke is among the leading causes of long-term disabilities leaving an increasing number
of people with cognitive, affective and motor impairments depending on assistance in their …

This work aims to find discriminative EEG-based features and appropriate classification
methods that can categorise brainwave patterns based on their level of activity or frequency …

Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke
patients when applied over a large number of sessions. Here we evaluated the effect and …