Making brain–machine interfaces robust to future neural variability
Abstract
A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training...
Paper Details
Title
Making brain–machine interfaces robust to future neural variability
Published Date
Dec 13, 2016
Journal
Volume
7
Issue
1
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