Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning

Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning

The primary goal of this work was to apply data-driven machine learning regression to assess if resting state functional connectivity (rs-FC) could estimate measures of behavioral domains in stroke subjects who completed brain-computer interface (BCI) intervention for motor rehabilitation. The study...

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Bibliographic Details
Main Authors: Rosaleena Mohanty, Anita M. Sinha, Alexander B. Remsik, Keith C. Dodd, Brittany M. Young, Tyler Jacobson, Matthew McMillan, Jaclyn Thoma, Hemali Advani, Veena A. Nair, Theresa J. Kang, Kristin Caldera, Dorothy F. Edwards, Justin C. Williams, Vivek Prabhakaran
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-09-01
Series:Frontiers in Neuroscience
Subjects:
brain-computer interface
stroke recovery
functional connectivity
motor impairment
machine learning
support vector regression
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2018.00624/full

Internet

https://www.frontiersin.org/article/10.3389/fnins.2018.00624/full

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