title: Inferring the stability of LIFE through Brain Machine Interfaces.
abstract: We examine neural signals from Longitudinally implanted Intra-Fascicular Electrodes (LIFE) in a chronic, rabbit model. Translation-invariant wavelet de-noising methods are used to improve S%R. Then traditional template-based spike sorting is applied to discriminate single units. We investigate the effect of discriminating between identified units on Brain Machine Interface (BMI) decoding performance. We infer the stability of LIFE based on decoding performance with and without current BMI methods to counter-act electrode neural signal degradation.
authors.affiliation: Dept. of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA. jfd134@ufl.edu
authors.forename: Jack
authors.initials: J
authors.lastname: Digiovanna
authors.forename: Luca
authors.initials: L
authors.lastname: Citi
authors.forename: Ken
authors.initials: K
authors.lastname: Yoshida
authors.forename: Jacopo
authors.initials: J
authors.lastname: Carpaneto
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
authors.forename: Justin C
authors.initials: JC
authors.lastname: Sanchez
authors.forename: Silvestro
authors.initials: S
authors.lastname: Micera
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D004567:Electrodes, Implanted; D004594:Electrophysiology / Q000379:methods; D005260:Female; D008959:Models, Neurological; D018482:Muscle, Skeletal / Q000294:innervation*; D011817:Rabbits; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D014584:User-Computer Interface