Neural prosthetic control signals from plan activity : NeuroReport (original) (raw)

MOTOR SYSTEMS

Shenoy, Krishna V.1 2; Meeker, Daniella3; Cao, Shiyan4; Kureshi, Sohaib A.1 5 6; Pesaran, Bijan1 7; Buneo, Christopher A.1; Batista, Aaron P.3 8; Mitra, Partha P.9; Burdick, Joel W.4; Andersen, Richard A.1 3 CA

1Division of Biology

3Computation and Neural Systems Program

4Division of Engineering and Applied Science

7Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125

5Division of Neurosurgery, Duke University, Durham, NC 27707

9Bell Labs, Lucent Technologies, Murray Hill, NJ 07974, USA

2Present address: Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, CA 94305, USA

6Present address: Neurosurgical Medical Clinic, Inc., San Diego, CA 92103, USA

8Present address: Howard Hughes Medical Institute and Department of Neurobiology, Stanford University, Stanford, CA 94305, USA

CACorresponding Author: [email protected]

Abstract

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.