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- William Bishop, Cynthia C Chestek, +5 authors Byron M Yu
- Journal of neural engineering
- 2014

OBJECTIVE
Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete… (More)

- William Bishop, Robert Armiger, +7 authors Stuart Harshbarger
- Conference proceedings : ... Annual International…
- 2008

We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. The VIE is a software environment that modularizes the core functions of a neural prosthetic system--receiving signals, decoding signals and controlling a real or simulated device. Complete prosthetic systems can be quickly assembled by linking… (More)

- Justin Baker, William Bishop, Spencer Kellis, Todd Levy, Paul House, Bradley Greger
- Conference proceedings : ... Annual International…
- 2009

We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey's trained hand. We also implanted a microwire electrocorticography grid (microECoG) epidurally so that it… (More)

- Robert S. Armiger, Francesco V. Tenore, +5 authors Stuart D. Harshbarger
- 2011

198 INTRODUCTION Within the last decade, neural prosthetics have seen tremendous progress based on advances in neuroscience, robotics, controls, electrical engineering, and applied mathematics. Virtual and real devices can now be controlled with signals recorded from a variety of locations along the neural efferent pathway from the brain to the hand. These… (More)

- William Bishop, Byron M Yu, +6 authors Stuart Harshbarger
- Conference proceedings : ... Annual International…
- 2008

We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. This paper, the second of two companion articles, describes the use of the VIE as a common platform for the implementation of neural decode algorithms. In this paper, a linear filter decode and a recursive Bayesian algorithm are implemented as… (More)

- William E. Bishop, Byron M. Yu
- NIPS
- 2014

We consider the problem of recovering a symmetric, positive semidefinite (SPSD) matrix from a subset of its entries, possibly corrupted by noise. In contrast to previous matrix recovery work, we drop the assumption of a random sampling of entries in favor of a deterministic sampling of principal submatrices of the matrix. We develop a set of sufficient… (More)

- Curtis C Travis, William E Bishop, David P Clarke
- Ecotoxicology
- 2003

The first sequencing of a complete organism genome occurred in 1995. Since then there has been an explosion of information, with a new organism being sequenced nearly every week. This rapid development of genomics is providing unparalleled opportunities in toxicology, ecology, and risk assessment. This paper provides an overview of some possible… (More)

- James Robert White, Todd Levy, William Bishop, James D. Beaty
- PloS one
- 2010

BACKGROUND
The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to… (More)

The Mixture of Trajectory Models (MTM) decoder has been used to reconstruct arm trajectories from neural activity. While it produces reasonable results, the computational demands of previously published versions may be too high for many real-time systems. We have developed a novel method of approximating the MTM state posteriors that does not require the… (More)

- William Bishop, Byron Yu
- 2015

First, note that by assumption rank{A} > 0. Let Ω1 = ρ1 × ρ1 and Ω2 = ρ2 × ρ2 be the two index sets in the theorem. By assumption we have ρ1 × ρ1 ∪ ρ2 × ρ2 = Ω and Ω 6= [n]× [n]. If A1 is not met, then ρ1 ∪ ρ2 6= [n], and from lemma 6 we can conclude recovery of A is impossible. If ρ1 ∪ ρ2 = [n], but A2 is not met then ι2 = |ρ1 ∩ ρ2| < r so it must be that… (More)