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

Neural Computation
David M BrandmanLeigh R Hochberg

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 Bayesian filtering framework to mitigate the effects of commonly encountered nonstationarities. Given a supervised training set of (neural features, intention to move in a direction)-pairs, we use gaussian process regression to predict the intention given the neural data. We apply kernel embedding for each neural feature with the standard radial basis function. The multiple kernels are then summed together across each neural dimension, which allows...Continue Reading

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Citations

Oct 14, 2020·Journal of Neural Engineering·Daniel SumaBin He
Aug 5, 2020·Nature Biomedical Engineering·Nir Even-ChenKrishna V Shenoy

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Software Mentioned

xPC
Matlab

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