A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent

A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent

Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical trans...

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Bibliographic Details
Main Authors: Nicholas D. Skomrock, Michael A. Schwemmer, Jordyn E. Ting, Hemang R. Trivedi, Gaurav Sharma, Marcia A. Bockbrader, David A. Friedenberg
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-10-01
Series:Frontiers in Neuroscience
Subjects:
brain-computer interface
decoding
machine learning
deep learning
support vector machines
response time
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2018.00763/full

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https://www.frontiersin.org/article/10.3389/fnins.2018.00763/full

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