Likelihood Gradient Ascent (LGA): a closed-loop decoder adaptation algorithm for brain-machine interfaces

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Siddharth DangiJose M Carmena

Abstract

Closed-loop decoder adaptation (CLDA) is an emerging paradigm for improving or maintaining the online performance of brain-machine interfaces (BMIs). Here, we present Likelihood Gradient Ascent (LGA), a novel CLDA algorithm for a Kalman filter (KF) decoder that uses stochastic, gradient-based corrections to update KF parameters during closed-loop BMI operation. LGA's gradient-based paradigm presents a variety of potential advantages over other "batch" CLDA methods, including the ability to update decoder parameters on any time-scale, even on every decoder iteration. Using a closed-loop BMI simulator, we compare the LGA algorithm to the Adaptive Kalman Filter (AKF), a partially gradient-based CLDA algorithm that has been previously tested in non-human primate experiments. In contrast to the AKF's separate mean-squared error objective functions, LGA's update rules are derived directly from a single log likelihood objective, making it one step towards a potentially optimal continuously adaptive CLDA algorithm for BMIs.

Citations

Aug 1, 2014·Frontiers in Systems Neuroscience·Zheng Li
Oct 14, 2014·Journal of Neural Engineering·Paul NuyujukianKrishna V Shenoy

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