Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model.

ABSTRACT: Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specifi

SUBMITTER: Willett FR 

PROVIDER: S-EPMC6586941 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature