Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces

Neural Computation
Siddharth DangiJose M Carmena

Abstract

Closed-loop decoder adaptation (CLDA) is an emerging paradigm for both improving and maintaining online performance in brain-machine interfaces (BMIs). The time required for initial decoder training and any subsequent decoder recalibrations could be potentially reduced by performing continuous adaptation, in which decoder parameters are updated at every time step during these procedures, rather than waiting to update the decoder at periodic intervals in a more batch-based process. Here, we present recursive maximum likelihood (RML), a CLDA algorithm that performs continuous adaptation of a Kalman filter decoder's parameters. We demonstrate that RML possesses a variety of useful properties and practical algorithmic advantages. First, we show how RML leverages the accuracy of updates based on a batch of data while still adapting parameters on every time step. Second, we illustrate how the RML algorithm is parameterized by a single, intuitive half-life parameter that can be used to adjust the rate of adaptation in real time. Third, we show how even when the number of neural features is very large, RML's memory-efficient recursive update rules can be reformulated to also be computationally fast so that continuous adaptation is stil...Continue Reading

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Citations

Dec 3, 2014·Neuron·Krishna V Shenoy, Jose M Carmena
Jun 2, 2015·PLoS Computational Biology·Josh MerelLiam Paninski
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Jan 22, 2015·Journal of Neural Engineering·P T SadtlerA P Batista
May 31, 2018·PLoS Computational Biology·Han-Lin Hsieh, Maryam M Shanechi
Feb 27, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Fabio RizzoglioFerdinando A Mussa-Ivaldi

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