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Benjamin I. Rapoport

Researcher at Massachusetts Institute of Technology

Publications -  19
Citations -  750

Benjamin I. Rapoport is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Adaptive algorithm & Micropower. The author has an hindex of 11, co-authored 17 publications receiving 690 citations. Previous affiliations of Benjamin I. Rapoport include Harvard University & Cornell University.

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Journal ArticleDOI

A Glucose Fuel Cell for Implantable Brain–Machine Interfaces

TL;DR: An implantable fuel cell that generates power through glucose oxidation, producing steady-state power and up to peak power is developed, demonstrating computationally that the natural recirculation of cerebrospinal fluid around the human brain theoretically permits glucose energy harvesting at a rate on the order of at least 1 mW with no adverse physiologic effects.
Journal ArticleDOI

Metabolic factors limiting performance in marathon runners.

TL;DR: The analytic approach presented here is used to estimate the distance at which runners will exhaust their glycogen stores as a function of running intensity, and provides a basis for guidelines ensuring the safety and optimizing the performance of endurance runners, both by setting personally appropriate paces and by prescribing midrace fueling requirements for avoiding ‘the wall.’
Journal ArticleDOI

Low-Power Circuits for Brain–Machine Interfaces

TL;DR: This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson's disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems.
Journal ArticleDOI

Real-Time Brain Oscillation Detection and Phase-Locked Stimulation Using Autoregressive Spectral Estimation and Time-Series Forward Prediction

TL;DR: This work characterized the algorithm's phase-locking performance on physiologic theta oscillations: optimizing algorithm parameters on the first patient using a genetic algorithm, and carried out cross-validation procedures on subsequent trials and electrodes within the same patient, as well as on data recorded from the second patient.
Proceedings ArticleDOI

Low-Power Circuits for Brain-Machine Interfaces

TL;DR: This paper presents work on ultra-low-power circuits for brain-machine interfaces with applications for paralysis prosthetics, prosthetics for the blind, and experimental neuroscience systems, including circuits for wireless stimulation of neurons.