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 Repjournal_title
Scientific reportsauthors
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 Adoi
10.1038/s41598-019-44166-7subject
Has Abstractpub_date
2019-06-20 00:00:00pages
8881issue
1issn
2045-2322pii
10.1038/s41598-019-44166-7journal_volume
9pub_type
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