scispace - formally typeset
M

Meel Velliste

Researcher at University of Pittsburgh

Publications -  27
Citations -  4533

Meel Velliste is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Population & Robotic arm. The author has an hindex of 19, co-authored 27 publications receiving 4142 citations. Previous affiliations of Meel Velliste include Carnegie Mellon University.

Papers
More filters
Journal ArticleDOI

Cortical control of a prosthetic arm for self-feeding

TL;DR: A system that permits embodied prosthetic control is described and monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task, and this demonstration of multi-degree-of-freedom embodied prosthetics control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.
Journal ArticleDOI

High-performance neuroprosthetic control by an individual with tetraplegia

TL;DR: With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.
Journal ArticleDOI

Functional network reorganization during learning in a brain-computer interface paradigm

TL;DR: A paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail is introduced, finding that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.
Journal ArticleDOI

Comparison of brain---computer interface decoding algorithms in open-loop and closed-loop control

TL;DR: By comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles.
Journal ArticleDOI

Robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images

TL;DR: Improved numeric features for describing subcellular location are reported here that are fairly robust to image intensity binning and spatial resolution and validated by using them to train neural networks that accurately recognize all major sub cellular patterns with an accuracy higher than any previously reported.