Background Significant clinician training is required to mitigate the subjective nature and
achieve useful reliability between measurement occasions and therapists. Previous …

Electroencephalography (EEG) and electromyography (EMG) are widespread and well-
known quantitative techniques used for gathering biological signals at cortical and muscular …

Objective. Convolutional neural networks (CNNs) have proven successful as function
approximators and have therefore been used for classification problems including …

One of the current challenges for translational rehabilitation research is to develop the
strategies to deliver accurate evaluation, prediction, patient selection, and decision-making …

Abstract Background Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke
patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid …

Severe impairment of limb movement after stroke can be challenging to address in the
chronic stage of stroke (eg, greater than 6 months post stroke). Recent evidence suggests …

Introduction Brain-computer interfaces (BCIs) have the potential in providing neurofeedback
for stroke patients to improve motor rehabilitation. However, current BCIs often only detect …

More than 85% of stroke survivors suffer from different degrees of disability for the rest of
their lives. They will require support that can vary from occasional to full time assistance …

Motor impairment after stroke is generally caused by damage to the neural networks that
control movement. Corticomuscular coherence (CMC) is a valid method to analyze the …

Hybrid Brain–Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should
enable the reinforcement of “more normal” brain and muscular activity. Here, we propose the …