title: Model-based decoding of reaching movements for prosthetic systems.
abstract: Model-based decoding of neural activity for neuroprosthetic systems has been shown, in simulation, to provide significant gain over traditional linear filter approaches. We tested the model-based decoding approach with real neural and behavioral data and found a 18% reduction in trajectory reconstruction error compared with a linear filter. This corresponds to a 40% reduction in the number of neurons required for equivalent performance. The model-based approach further permits the combination of target-tuned plan activity with movement activity. The addition of plan activity reduced reconstruction error by 23% relative to the linear filter, corresponding to 55% reduction in the number of neurons required. Taken together, these results indicate that a decoding algorithm employing a prior model of reaching kinematics can substantially improve trajectory estimates, thereby improving prosthetic system performance.
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
authors.forename: Caleb
authors.initials: C
authors.lastname: Kemere
authors.forename: Gopal
authors.initials: G
authors.lastname: Santhanam
authors.forename: Byron M
authors.initials: BM
authors.lastname: Yu
authors.forename: Stephen
authors.initials: S
authors.lastname: Ryu
authors.forename: Teresa
authors.initials: T
authors.lastname: Meng
authors.forename: Krishna V
authors.initials: KV
authors.lastname: Shenoy