The impact of command signal power distribution processing delays and speed scaling on neurally-controlled devices

摘要

ObjectiveDecoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: 1) error magnitude, 2) distribution of different frequency components in the decoding errors, 3) processing delays, and 4) command gain.