Detecting neural state transitions using hidden Markov models for motor cortical prostheses

  • Kemere C
  • Santhanam G
  • Yu B
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Neural prosthetic interfaces use neural activity related to the planning and peri-movement epochs of arm-reaching to enable brain-directed control of external devices. Previous research has primarily centered on accurately decoding movement intention from either plan or peri-movement activity, but has assumed that temporal boundaries between these epochs are known to the decoding system. In this work, we develop a technique to automatically differentiate between baseline, plan, and peri-movement epochs of neural activity. Specifically, we use a generative model of neural activity to capture how neural activity varies between these three epochs. Our approach is based on a hidden Markov model {(HMM),} in which the latent variable (state) corresponds to the epoch of neural activity, coupled with a state-dependent Poisson firing model. Using an {HMM} and simultaneous 96-electrode recordings from the premotor cortex of rhesus monkeys, we demonstrate that the time of transition from baseline to plan epochs, a transition in neural activity which is not accompanied by any external behavior changes, can be detected using a threshold on the {\textbackslash}emph{a posteriori} {HMM} state probabilities. Following detection of the plan epoch, we show that the intended target of a center-out movement can be detected about as accurately as a maximum likelihood estimator using a window of known plan activity. In addition, we demonstrate that our {HMM} can detect transitions in neural activity corresponding to targets not found in training data. Thus, the {HMM} technique for automatically detecting transitions between epochs of neural activity should enable prosthetic interfaces to operate autonomously.

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  • Caleb Kemere

  • Gopal Santhanam

  • Byron M Yu

  • Afsheen Afshar

  • Stephen I Ryu

  • Teresa H Meng

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