Exploring time-scales of closed-loop decoder adaptation in brain-machine interfaces

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Amy L OrsbornJ M Carmena

Abstract

Performing closed-loop modifications of a brain-machine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that our experimental training models are representative of a broad range of paralyzed patients, our decoders were initially trained using neural activity recorded during subject observation of cursor movement. We find that both closed-loop adaptation algorithms can be used to boost BMI performance from 20-30% to 80%, yielding movement kinematics similar to natural arm movements. Based on insights derived from the performance of each algorithm, we propose that a hybrid of batch and online decoder adaptation may be the best approach.

Citations

Jul 10, 2012·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Amy L OrsbornJose M Carmena
Jul 9, 2016·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Charles B MatlackChet T Moritz
Mar 10, 2017·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·David M BrandmanLeigh R Hochberg
Jan 25, 2018·Journal of Neural Engineering·David M BrandmanLeigh R Hochberg
Oct 14, 2014·Journal of Neural Engineering·Paul NuyujukianKrishna V Shenoy
May 13, 2020·Journal of Neuroengineering and Rehabilitation·Dalia De Santis, Ferdinando A Mussa-Ivaldi
Feb 27, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Fabio RizzoglioFerdinando A Mussa-Ivaldi

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