Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression.

ABSTRACT: Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has been dedicated to making the decoding algorithm more resilient to signal nonstationarities. Here, we describe how principled kernel selection with gaussian process regression can be used within a Bayes

SUBMITTER: Brandman DM 

PROVIDER: S-EPMC6685768 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature