Andrew B. Schwartz

Affiliations:
  • University of Pittsburgh, Department of Bioengineering, Pittsburgh, PA, USA
  • Carnegie Mellon University, Robotics Institute, Pittsburgh, PA, USA
  • Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
  • University of Minnesota, Minneapolis, MN, USA (PhD 1984)


According to our database1, Andrew B. Schwartz authored at least 14 papers between 2007 and 2019.

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Bibliography

2019
Prolonged functional optical sensitivity in non-human primate motor nerves following cyclosporine-based immunosuppression and rAAV2-retro mediated expression of ChR2.
Proceedings of the 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019

2017
Intracortical Microstimulation as a Feedback Source for Brain-Computer Interface Users.
Proceedings of the Brain-Computer Interface Research - A State-of-the-Art Summary 6, 2017

Autonomy infused teleoperation with application to brain computer interface controlled manipulation.
Auton. Robots, 2017

2016
Idle state classification using spiking activity and local field potentials in a brain computer interface.
Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016

2015
Autonomy Infused Teleoperation with Application to BCI Manipulation.
Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015

2014
Single-Snippet Analysis for Detection of Postspike Effects.
Neural Comput., 2014

2013
Progress toward a high-performance neural prosthetic.
Proceedings of the International Winter Workshop on Brain-Computer Interface, 2013

2012
Bayesian learning in assisted brain-computer interface tasks.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

State-space control of prosthetic hand shape.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

2010
Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control.
J. Comput. Neurosci., 2010

2009
Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms.
Neural Networks, 2009

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008
Population vector code: a geometric universal as actuator.
Biol. Cybern., 2008

2007
Statistical Signal Processing and the Motor Cortex.
Proc. IEEE, 2007


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