Neural prosthetic control signals from plan activity

Krishna V. Shenoy, Daniella Meeker, Shiyan Cao, Sohaib A. Kureshi, Bijan Pesaran, Christopher A. Buneo, Aaron P. Batista, Partha P. Mitra, Joel W. Burdick, Richard A. Andersen

Research output: Contribution to journalArticlepeer-review


The prospect of assisting disabled patients by translating neural activity from the brain into control signals for prosthetic devices, has flourished in recent years. Current systems rely on neural activity present during natural arm movements. We propose here that neural activity present before or even without natural arm movements can provide an important, and potentially advantageous, source of control signals. To demonstrate how control signals can be derived from such plan activity we performed a computational study with neural activity previously recorded from the posterior parietal cortex of rhesus monkeys planning arm movements. We employed maximum likelihood decoders to estimate movement direction and to drive finite state machines governing when to move. Performance exceeded 90% with as few as 40 neurons.

Original languageEnglish (US)
Pages (from-to)591-596
Number of pages6
Issue number4
StatePublished - Mar 2003


  • Bayesian decoders
  • Brain-computer interfaces
  • Finite state machines
  • Maximum likelihood
  • Neural prosthetic systems
  • Posterior parietal cortex

ASJC Scopus subject areas

  • General Neuroscience


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