Objective: Paralyzed patients may benefit from restoration of movement afforded by prosthetics
controlled by electrocorticography (ECoG). Although ECoG shows promising results in …

Object A brain-machine interface (BMI) offers patients with severe motor disabilities greater
independence by controlling external devices such as prosthetic arms. Among the available …

Brain-machine interface techniques have been applied in a number of studies to control
neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore …

Seizure detection using intracranial electroencephalography (iEEG) contributes to improved
treatment of patients with refractory epilepsy. For that purpose, a feature of iEEG to …

The cause of pain in a phantom limb after partial or complete deafferentation is an important
problem. A popular but increasingly controversial theory is that it results from maladaptive …

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method
to predict tumor genotypes using a pretrained convolutional neural network (CNN) from …

The application of deep learning to neuroimaging big data will help develop computer-aided
diagnosis of neurological diseases. Pattern recognition using deep learning can extract …

Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients.
Invasive measurements of cortical currents using electrocorticography have been widely …

Seeking to apply brain–machine interface technology in neuroprosthetics, a number of
methods for predicting trajectory of the elbow and wrist have been proposed and have shown …

Objective. Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals
have been developed to restore the communication function of severely paralyzed patients. …