Latest AI and machine learning research in neurology for healthcare professionals.
Development of noninvasive brain-machine interface (BMI) systems based on electroencephalography (EEG), driven by spontaneous movement intentions, is a useful tool for controlling external devices or supporting a neuro- rehabilitation. In this study, we present the possibility of brain-controlled robot arm system using arm trajectory decoding. To do that, we first constructed the experimental syst...
Motor Imagery (MI) is a typical paradigm for Brain-Computer Interface (BCI) system. In this paper, we propose a new framework by introducing a tensor-based feature representation of the data and also utilizing a convolutional neural network (CNN) architecture for performing classification of MI-EEG signal. The tensor-based representation that includes the structural information in multi-channel ti...
Parkinson's Disease (PD) is a neurodegenerative disorder that manifests through slowly progressing symptoms, such as tremor, voice degradation and bra...
There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictio...
BACKGROUND: Prone mobility, central to development of diverse psychological and social processes that have lasting effects on life participation, is s...
Our research team has developed two versions of an ankle robot for children with cerebral palsy. Both devices provide three degrees of freedom and are...
Rehabilitation robotics is an emerging field in which gait training has been largely automated allowing more intensive, repetitive motions which are i...
In this paper, we present the new personalized 3D printed soft robotic hand for providing rehabilitation training and daily activities assistance to s...
Rehabilitative exercise for people suffering from upper limb impairments has the potential to improve their neuro-plasticity due to repetitive trainin...
Single-sided motor weakness, also known as hemiparesis, is the most prevalent gait impairment among stroke survivors, which often results in gait asym...
In this paper, we propose a wrist rehabilitation robot employing a novel actuation mechanism composed of electromagnetic clutch, brake, and motor and ...
A key feature of a successful game is its ability to provide the player with an adequate level of challenge. However, the objective of difficulty adap...
Stroke is one of the leading causes of impairment in the world. Many of those who have suffered a stroke experience long-term loss of upper-limb funct...
In an attempt to promote greater functional recovery after spinal cord injury, researchers have begun exploring combinatorial treatments, such as robo...
Hand function is often impaired after neurological injuries such as stroke. In order to design patient-specific rehabilitation, it is essential to qua...
Robot-assisted rehabilitation in children and young adults with Cerebral Palsy (CP) is expected to lead to neuroplasticity and reduce the burden of mo...
Robot-based neurorehabilitation strategies often ignore cognitive performance during treatment, but this is a need in populations dealing with a wide ...
Postural responses to unstable conditions or perturbations are important predictors of the risk of falling and can reveal balance deficits in people w...
Proprioceptive deficits are common among stroke survivors and are associated with slower motor recovery, poorer upper limb motor function, and decreas...
Robot-assisted rehabilitation of hand function is becoming an established approach to complement conventional therapy after stroke, particularly in vi...