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Emery N. Brown
Researcher at Massachusetts Institute of Technology
Publications - 599
Citations - 37710
Emery N. Brown is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Burst suppression & Spike train. The author has an hindex of 89, co-authored 571 publications receiving 32588 citations. Previous affiliations of Emery N. Brown include Boston University & United States Department of Veterans Affairs.
Papers
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Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression
Giorgio Bonmassar,Catherine Poulsen,Eric T. Pierce,Patrick L. Purdon,Pavitra Krishnaswamy,Emery N. Brown +5 more
TL;DR: This work model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts that outperforms commonly used reference-based and component analysis techniques.
Proceedings ArticleDOI
A probabilistic framework for learning robust common spatial patterns
TL;DR: This work presents a robust algorithm based on the reformulation of a well-known spatial filtering and feature extraction algorithm named Common Spatial Patterns (CSP), and casts the problem of learning CSP into a probabilistic framework, which allows for insights into the algorithm.
nSTAT: Open-source neural spike train analysis toolbox for Matlab
TL;DR: NSTAT is developed--an open source neural spike train analysis toolbox for Matlab® that provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process model of neural function.
Likelihood Methods for Point Processes with Refractoriness
TL;DR: An approximation to the likelihood of a point-process model of neurons that holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity function, and its integrability is proposed.
State-Space Algorithms for Estimating Spike Rate Functions
Anne C. Smith,João Domingos Scalon,Sylvia Wirth,Marianna Yanike,Wendy A. Suzuki,Emery N. Brown +5 more
TL;DR: In this article, a state-space model for estimating the spike rate function that provides amaximum likelihood estimate of the spike rates, model goodness-of-fit assessments, as well as confidence intervals for the Spike rate function and any other associated quantities of interest.