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Samuel R. Nason

Researcher at University of Michigan

Publications -  21
Citations -  309

Samuel R. Nason is an academic researcher from University of Michigan. The author has contributed to research in topics: Computer science & Amplifier. The author has an hindex of 6, co-authored 16 publications receiving 118 citations.

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Journal ArticleDOI

A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces

TL;DR: It is shown that the 300–1,000 Hz band of spiking activity, dominated by local single-unit spikes, can enhance the decoding performance of neural interfaces and correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR.
Journal ArticleDOI

Neural control of finger movement via intracortical brain-machine interface

TL;DR: This is the first demonstration of brain control of finger-level fine motor skills in rhesus macaques and it is believed that these results represent an important step towards full and dexterous control of neural prosthetic devices.
Proceedings ArticleDOI

26.9 A 0.19×0.17mm 2 Wireless Neural Recording IC for Motor Prediction with Near-Infrared-Based Power and Data Telemetry

TL;DR: This paper proposes an electrocorticography (ECoG) recording system with near-field RF power transfer and bilateral communication, but the 0.5W Tx exceeds maximum exposure limits by 10x and computes so-called spiking band power on-chip to save 920x power while maintaining accurate finger position and velocity decoding.
Journal ArticleDOI

Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter.

TL;DR: This is the first systematic and biomimetic separation of digits for continuous online decoding in a NHP as well as the first demonstration of the ReFIT Kalman filter improving the performance of precise finger decoding.
Journal ArticleDOI

The future of upper extremity rehabilitation robotics: research and practice.

TL;DR: The findings show that peripheral nerve interfaces and brain‐machine interfaces have many similar characteristics that enable them to be concurrently developed and may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.