Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform
We developed a portable, low-cost (~$310) BCI and compared its performance to that of a conventional BCI which was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen.
Design and Characterization of an Exoskeleton for Perturbing the Knee During Gait
We aim to probe the neuromechanics underpinning human locomotion. This information can be used to improve our understanding of typical and atypical joint behaviors, and to improve the design and control of gait assistive devices.
Robust Estimation of Sparse Narrowband Spectra from Neuronal Spiking Data
In this paper, we address the problem of estimating the power spectral density of the neural covariate driving the spiking statistics of a neuronal population, from binary observations.
Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping
We use non-invasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological model for predicting the response to different pacing conditions. An efficient forward model is proposed, coupling a transmembrane potential model with a current dipole formulation…
Glucose Monitoring in Individuals With Diabetes Using a Long-Term Implanted Sensor/Telemetry System and Model
We have developed a fully implanted battery-operated sensor/telemetry system that transmits glucose values to an external receiver. Sensors were implanted in five individuals with diabetes using a simple outpatient procedure, and operated for up to six months. The results demonstrate the feasibility of long-term, continuous monitoring of glucose in humans.
Improvement of Pyramidal Tract Side Effect Prediction Using a Data-Driven Method in Subthalamic Stimulation
Subthalamic nucleus deep brain stimulation is limited by the occurrence of pyramidal tract side effect induced by electrical activation of the pyramidal tract. Previous biophysical models to predict this effect are either inaccurate or time consuming. In this paper, we proposed a data driven based method that enables real time computing and with enough accuracy for the use in clinical practice. Compared to the biophysical method, the data-driven method showed results significantly better in order to predict pyramidal tract side effect.