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

Brain-computer interfaces (BCIs) have undergone significant advancements in recent years.
The integration of deep learning techniques, specifically transformers, has shown promising …

Objective. Electroencephalography (EEG) motor imagery classification has been widely
used in healthcare applications such as mobile assistive robots and post-stroke …

Objective. Lower-limb, powered robotics systems such as exoskeletons and orthoses have
emerged as novel robotic interventions to assist or rehabilitate people with walking …

Humans interact with computers through various devices. Such interactions may not require
any physical movement, thus aiding people with severe motor disabilities in communicating …

This article presents a rehabilitation technique based on a lower-limb exoskeleton
integrated with a human–machine interface (HMI). HMI is used to record and process …

We show that persons with spinal cord injury (SCI) retain decodable neural correlates of
attempted arm and hand movements. We investigated hand open, palmar grasp, lateral …

Electroencephalography (EEG) signals have great impact on the development of assistive
rehabilitation devices. These signals are used as a popular tool to investigate the functions …

Background The incidence and mortality rates of stroke are escalating due to the growing
aging population, which presents a significant hazard to human health. In the realm of …

Since the first robotic exoskeleton was developed in 1960, this research field has attracted
much interest from both the academic and industrial communities resulting in scientific …