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-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Willett FR,Young DR,Murphy BA,Memberg WD,Blabe CH,Pandarinath C,Stavisky SD,Rezaii P,Saab J,Walter BL,Sweet JA,Miller JP,Henderson JM,Shenoy KV,Simeral JD,Jarosiewicz B,Hochberg LR,Kirsch RF,Bolu Ajiboye A

doi

10.1038/s41598-019-44166-7

subject

Has Abstract

pub_date

2019-06-20 00:00:00

pages

8881

issue

1

issn

2045-2322

pii

10.1038/s41598-019-44166-7

journal_volume

9

pub_type

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