title: Exploring time-scales of closed-loop decoder adaptation in brain-machine interfaces.
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.
authors.affiliation: University of California, Berkeley -Unviversity of California, San Francisco Graduate Program in Bioengineering, University of California, Berkeley, CA 94729, USA. amyorsborn@berkeley.edu
authors.forename: Amy L
authors.initials: AL
authors.lastname: Orsborn
authors.forename: Siddharth
authors.initials: S
authors.lastname: Dangi
authors.forename: Helene G
authors.initials: HG
authors.lastname: Moorman
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carmena
mesh_terms: D000465:Algorithms; D000818:Animals; D001132:Arm / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005246:Feedback; D006801:Humans; D008253:Macaca mulatta; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D014584:User-Computer Interface