A Novel Validation Approach for High-Density Surface EMG Decomposition in Motor Neuron Disease
This paper presents a novel two-source approach for validating the performance of high-density surface electromyogram (EMG) decomposition. The approach was developed taking advantage of surface EMG characteristics of amyotrophic lateral sclerosis (ALS).
StableEyes—A Portable Vestibular Rehabilitation Device
The vestibulo-ocular reflex (VOR) is the primary mechanism for stabilizing vision during rapid head movements. We have developed a training technique that typically increases the VOR response a minimum of 15% after 15 mins of training.
Transcutaneous Electrical Spinal Stimulation Promotes Long-Term Recovery of Upper Extremity Function in Chronic Tetraplegia
Upper extremity function is the highest priority of tetraplegics for improving quality of life. We aim to determine the therapeutic potential of transcutaneous electrical spinal cord stimulation for restoration of upper extremity function.
Call for Papers: Special Issue on Neural Systems Engineering and Mathematical Modelling of Brain dynamics using ECoG/EEG/MEG oscillations and Machine learning methods
Submission deadline extended to July 1, 2018! In this special issue, Guest Editor Steve Mehrkanoon, PhD, welcomes papers that address many of the challenges of mathematical and computational models of the brain networks and dynamics given the measurement data ECoG/EEG/MEG.
Effect of the Synchronization-Based Control of a Wearable Robot Having a Non-Exoskeletal Structure on the Hemiplegic Gait of Stroke Patients
We have been developing the robotic wear curara as both a welfare device and rehabilitation robot that assists the elderly and disabled. curara is aimed at user friendliness. We have, thus, chosen a non-exoskeleton structure made of a plastic so that the robot is as light in weight as possible and to minimize the restraining stress against natural human movement.
Highly Efficient Compression Algorithms for Multichannel EEG
The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms.