23 citations · 28 across the 5 of their papers we have counts for
5 papers
Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors
Daniel Lopez-Martinez, Ke Peng, Arielle Lee +2
Currently self-report pain ratings are the gold standard in clinical pain assessment. However, the development of objective automatic measures of pain could substantially aid pain…
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
Daniel Lopez-Martinez, Ke Peng, Sarah C. Steele +3
Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measu…
A simple and objective method for reproducible resting state network (RSN) detection in fMRI
Gautam V. Pendse, David Borsook, Lino Becerra
Spatial Independent Component Analysis (ICA) decomposes the time by space functional MRI (fMRI) matrix into a set of 1-D basis time courses and their associated 3-D spatial maps th…
SMART: A statistical framework for optimal design matrix generation with application to fMRI
Gautam Pendse, Adam Schwarz, Richard Baumgartner +3
The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univari…
ADIS - A robust pursuit algorithm for probabilistic, constrained and non-square blind source separation with application to fMRI
Gautam Pendse, David Borsook, Lino Becerra
In this article, we develop an algorithm for probabilistic and constrained projection pursuit. Our algorithm called ADIS (automated decomposition into sources) accepts arbitrary no…