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SUMMARY:A brain-machine interface for locomotion driven by subspace dynami
 cs - Karen E. Schroeder\, Columbia University
DTSTART:20181121T110000Z
DTEND:20181121T120000Z
UID:TALK115162@talks.cam.ac.uk
CONTACT:Marcelo Gomes Mattar
DESCRIPTION:Brain-machine interfaces (BMIs) for reaching have enjoyed cont
 inued performance improvements\,  allowing remarkable 2D cursor control. Y
 et there remains significant clinical need for locomotor (e.g.\, wheelchai
 r control) BMIs\, which could benefit a larger patient population. Fewer s
 tudies have addressed this need\, and the best strategy for doing so remai
 ns undetermined. Here we demonstrate an approach based upon rhythmic neura
 l activity. We leverage a behavioral task wherein monkeys cycle a hand-hel
 d pedal\, forward or backward\, to advance along a virtual track\, pausing
  on targets for reward. This task does not emulate natural locomotion\, bu
 t rather provides a view of cortical activity during learned\, voluntary\,
  rhythmic movement. Such activity is robust and was recently characterized
  (Russo et al. 2018)\, affording opportunities to develop novel decode alg
 orithms and test them in an online setting.\nWe constructed a decoder that
  decoded virtual self-motion\, based on recordings from 192 electrodes imp
 lanted in motor cortex. Unlike algorithms for cursor control\, we did not 
 directly map neural states to commanded velocity or position. Instead\, we
  leveraged the most robust aspects of response structure: an overall shift
  in neural state when moving versus stationary\, and rotations of the neur
 al state while cycling. We used these features to decode when the subject 
 was moving\, and decoded direction based on the finding that neural-state 
 rotations occur in different planes during forward versus backward cycling
 . Perhaps because the subject need not learn a novel mapping to control th
 e BMI\, performance was high even during the first few sessions of brain c
 ontrol. An additional performance gain was obtained by leveraging a neural
  dimension that reflected cycling direction at movement initiation. Result
 ing brain-control success rates were very close to those achieved under ar
 m-control. Thus\, rhythmic neural activity provides a robust substrate for
  BMI control\, but requires different decode strategies than have been emp
 loyed previously.
LOCATION:Cambridge University Engineering Dept.\, CBL Seminar Room (4th fl
 oor)
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